which feature in the data to use to make a decision) and the number of splits and the respective split thresholds. Inducing cost-sensitive decision trees via instance weighting. Schapire, R. E. and Singer, Y. The experimental results show that the TBWC algorithm yields the highest accuracies when compared with decision tree learning and clustering for all datasets and improves the predictive performance especially for multi-class datasets. Artificial Intelligence 3rd Ed. A tree induction algorithm will typically use impurity to determine these factors. Learning decision trees using the area under the roc curve. 1990. The uppermost node in the tree is the root node.
Art. This paper will discuss scalability of decision tree algorithm based on the selection of the attribute selection measure, and perform the comparative analysis of these measures to determine which measure should be used in which situation in order to increase the Scalability of the Decision Tree algorithm.
Model. Learning on FPGA, 09/03/2020 by Zhe Lin Margineantu, D. and Dietterich, T. 2003. Bureau Standards 12, 36--38. Hart, A. E. 1985. Zadrozny, B., Langford, J., and Abe, N. 2003a. Missing is useful: Missing values in cost-sensitive decision trees. Two--way Contingency Tables. In Proceedings of the 19th Machine Learning International Workshop then Conference. 2005. Hunt, E. B., Marin, J., and Stone, P. J. In Proceedings of the 9th International Conference on Data Warehousing and Knowledge Discovery. The use of background knowledge in decision tree induction. Learning to predict channel stability using biogeomorphic features. In Proceedings of the 10th European Conference on Machine Learning (ECML '98). Engin. Esmeir, S. and Markovitch, S. 2004. Chapman and Hall/CRC, London. C. E. Brodley, Ed., 257--264. Each resulting bucket of data would be pure in the sense that each data point would belong to the same class. A User's Guide to GENESIS v5.0. Pruning decision trees with misclassification costs. Sometimes these simplified trees are merely the byproduct after a more complex decision tree has been pruned of anomalies and outliers in the training data. 929. Cost-Sensitive classification with respect to waiting cost. Transition from Categorical to Survival Data. Knowl. Re-designing cost-sensitive decision tree learning. 191, 1, 47--57. Ting, K. 2000a. Learning class-sensitive active classifiers. In Proceedings of the 11th International Joint Conference on Artificial Intelligence (IJCAI '89). In Proceedings of the 17th International Conference on Machine Learning (ICML '00). InProceedings of the Budgeted Learning Workshop(ICML '10). Murthy, S. and Salzberg, S. 1995. Domingos, P. 1999. In Proceedings of the ACM International Conference on Machine Learning. In Proceedings of the 14th International Joint Conference on Artificial Intelligence. International Journal of ADVANCED AND APPLIED SCIENCES. It is shown that the data structure can be used to manage a set of n k-dimensional records or data items such that the records can be searched or updated in O(log2 n) + k time, which is optimal. J. Artif. The data mining technique used in this paper is classification in order to compare the data mining tools and the evaluation criterion used for the evaluation of tool is classification accuracy. Technol. A simple method for cost-sensitive learning. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '04). Issues in building a scalable classifier are discussed and the design of SLIQ, a new classifier that uses a novel pre-sorting technique in the tree-growth phase to enable classification of disk-resident datasets is presented. Zadrozny, B., Langford, J., and Abe, N. 2003b. A decision-theoretic generalization of on-line learning and an application to boosting, J. Comput. Lomax, S. and Vadera, S. 2011. 14, 3, 659--665. Lin, F. Y. and Mcclean, S. 2000. 1, 81--106. 2002. 1025--1033. Loglinear Models. 2000. Freund, Y. and Schapire, R. E. 1997. Discov. Breiman, L., Friedman J. H., Olsen R. A., and Stone C. J. An empirical comparison of voting classification algorithms: Bagging, boosting and variants. 1951. Data Engin. 3720. 2., Morgan Kaufmann, 973--978. Intell. Res. Lecture Notes in Computer Science, vol. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '06). A., Brodley, C. E., and Utgoff, P. E. 1994. Proceedings Seventh International Workshop on Research Issues in Data Engineering. The past decade has seen a significant interest on the problem of inducing decision trees that take account of costs of misclassification and costs of acquiring the features used for decision making. Learning Machines.
http://robotics.stanford.edu/~ronnyk/prune-long.ps.gz. J. Knowl. Dong, M. and Kothari, R. 2001. Addison Wesley. ACM Trans. 2005. In Proceedings of the 6th International Workshop on Machine Learning (ML '89). Lookahead-Based algorithms for anytime induction of decision trees. Improved boosting algorithms using confidence-rated predictions. 1966. Li, J., Li, X., and Yao, X. 148--156. Methods for Recommending Classification Algorithms (Extended Version), 09/16/2020 by Mrcio P. Basgalupp 1996. 2005. 2002. 36, 1-2, 105--139. Doctoral thesis, Oregon State University. In Proceedings of the 1st International Conference on Discovery Science. 37, 3, 297--336.
Estruch, V., Ferri, C., Hernndez-Orallo, J., and Ramrez-Quintana, m. j. In Research and Development in Expert Systems. Nnez, M. 1991.
Mach. Intell. Data mining classification algorithms: An overview, Comparative Analysis of Decision Tree Classification Algorithms, A Survey on Decision Tree Algorithms of Classification in Data Mining, A combination of decision tree learning and clustering for data classification, Comparative analysis of attribute selection measures used for attribute selection in decision tree induction, Survey on Decision Tree Classification algorithms for the Evaluation of Student Performance, Classification Techniques in Machine Learning: Applications and Issues, Performance of Data Mining Tools: A Case Study Based on Classification Algorithms and Datasets, Decision tree methods: applications for classification and prediction, Educational Data Mining by Using Neural Network, Generalization and decision tree induction: efficient classification in data mining, SLIQ: A Fast Scalable Classifier for Data Mining, SPRINT: A Scalable Parallel Classifier for Data Mining, Decision Tree Construction from Multidimensional Structured Data, Data-Driven Discovery of Quantitative Rules in Relational Databases, International Journal of Computer and Electrical Engineering. Data Engin. Modelling by shortest data description. Mease, D., Wyner, A. J., and Buja, A. Lecture Notes in Computer Science, vol. In Proceedings of the 3rd IEEE International Conference on Data Mining. Ni, A., Zhang, S., Yang, S., and Zhu, X. A data classification method which integrates attribute oriented induction, relevance analysis, and the induction of decision trees is proposed, which leads to efficient, high quality, multiple level classification of large amounts of data. Ling, C. X., Yang, Q., Wang, J., and Zhang, S. 2004. 33--42. System, 12/11/2020 by Zhe Lin For instance if a single node was able to find a set of splits and thresholds that partitioned the data uniformly into some collection of 'buckets', then we would have a model that perfectly classifies the data. In Proceedings of the 11th International Conference on Machine Learning.
Lozano, A.C. and Abe, N. 2008. 1998a. In Workshop de Mineria de Datos y Aprendizaje. Quinlan, J. R. 1986. 1989. Copyright 2022 ACM, Inc. Abe, N., Zadrozny, B., and Langford, J. IEEE Trans. 16, 9, 888--893. Sci. Like all decision trees, this algorithm includes a root node, branches, and leaf nodes. Discovering rules by induction from large collections of examples. 383--386. Davis, J. V., Jungwoo, H., and Rossbach, C. J. Winston, P. H. 1993. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.27.1102. 983--990.
Ling, C., Sheng, V., and Yang, Q. Sci., Engin.
Esmeir, S. and Markovitch, S. 2008. In Proceedings of the 16th International Joint Conference on Artificial Intelligence (IJCAI99). In Proceedings of the International Conference on Computational Science (ICCS '02). http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.46.2272. Zhang, S., Zhu, X., Zhang, J., and Zhang, C. 2007. Afifi, A. 4, 1080--1085. 139, 2, 137--174. Vol. 2007. Morgan Kaufman, San Mateo, CA. Small nets and short paths: Optimizing neural computation. Zhang, S., Qin, Z., Ling, C., and Sheng, S. 2005.
Meir, R. and Rtsch, g. 2003. View 2 excerpts, references background and methods. 23, 5, 369--378. Learn.
We use cookies to ensure that we give you the best experience on our website. Inductive knowledge acquisition: A case study.
Lecture Notes in Computer Science, vol. 2012 International Conference on Radar, Communication and Computing (ICRCC). 2002. Cost-Sensitive concept learning of sensor use in approach and recognition. Hybrid cost-sensitive decision tree. Knowl. 97--105. Studies 27, 221--234. Res. Freund, Y. and Schapire, R. E. 1996. 22, 4, 206--221. Cost-Sensitive classification with genetic programming. J. Mach. A new cost-sensitive decision tree with missing values. In Proceedings of the 13th International Machine Learning Workshop then Conference. 2, 369--409. Bagging predictors. Quinlan, J. R., Compton, P. J., Horn, K. A., and Lazarus, L. 1987. Impurity is essentially measures how well each node with its corresponding splits and thresholds separates the data. Genetic Algorithms + Data Structures = Evolution Programs 3rd Ed.
1--8. This paper examines the various types of classification algorithms in Data Mining, their applications and categorically states the strengths and limitations of each type. Learning efficient classification procedures and their application to chess end games. Algorithms used to develop decision trees are introduced and the SPSS and SAS programs that can be used to visualize tree structure are described, including CART, C4.5, CHAID, and QUEST. 1998b.
2006b.
In Proceedings of the IEEE Congress on Evolutionary Computation. Vol 2, 1401--1406. 1994.
Lecture Notes in Computer Science, vol. McGraw-Hill, New York. Experiments with a new boosting algorithm. Logistic Regression Models. J. Artif. Data Engin. http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.7.7947. Experience in the use of an inductive system in knowledge engineering. 1532., Springer, 244--255. Learn. A Brief Introduction to Boosting. An introduction to boosting and leveraging. 2005. In Proceedings of the 10th European Conference on Machine Learning. Chapman & Hall, London. 18, Hard-ODT: Hardware-Friendly Online Decision Tree Learning Algorithm and Zhang, S. 2010. Cost-Sensitive decision tree learning for forensic classification. This alert has been successfully added and will be sent to: You will be notified whenever a record that you have chosen has been cited. 1998. McGraw-Hill, New York. 2114--2121. Grefenstette, J. J. Check if you have access through your login credentials or your institution to get full access on this article. Learn. Fuzzy Syst. Mach. Ting, K. M. and Zheng, Z. Evolutionary induction of decision trees for misclassification cost minimization. Intell. Springer. Schapire, R. E. 1999.
Omielan, A. 17, 12, 1689--1693. Shannon, C. E. 1948. Simplifying decision trees. Esmeir, S. and Markovitch, S. 2007. Murthy, S., Kasif, S., and Salzberg, S. 1994. 0, Fair Forests: Regularized Tree Induction to Minimize Model Bias, 12/21/2017 by Edward Raff The foundations of cost-sensitive learning. The mathematical theory of communication. Eds., Springer, 119--184. J. Artif. Ferri-Ramrez, C., Hernndez, J., and Ramirez, M. J. C4.5: Programs for Machine Learning. Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06). G. I. Webb and X. Yu, Eds., Lecture Notes in Artificial Intelligence, vol. The goal of this study is to provide a comprehensive review of different classification techniques in machine learning and will be helpful for both academia and new comers in the field of machine learning to further strengthen the basis of classification methods. Esmeir, S. and Markovitch, S. 2011. Mach. ACM, 155--164. Breiman, L. 1996. Engin. 4, 227--243. Kretowski, M. and Grzes, M. 2007. A new SPRINT decision tree algorithm is represented which will be used to solve the problems of classification in educational data system and is fast and scalable than others because it can be implemented in both serial and parallel fashion good data replacement and load balancing. Or after a larger decision tree has been optimized to reduce the number of leaves, or its cost complexity. Either approach often leaves the tree with a straightforward decision path with only a few options available at any given layer. An empirical comparison of cost-sensitive decision tree induction algorithms. Therefore we want to maximize the resulting purity after each node decision (equivalently we wish to minimize the impurity). CSNL: A cost-sensitive non-linear decision tree algorithm.
Knowl. J. Springer, 139--147. Index. Norton, S. W. 1989. 217--225. http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:Reducing+&q=intitle:Reducing++misclassification+costs#0. Various algorithms of Decision tree (ID3, C4.5, CART), their characteristic, challenges, advantage and disadvantage, are focused on. In Proceedings of the 2nd European Symposium on Principles of Data Mining and Knowledge Discovery. 4798, 447--459. Methods for cost-sensitive learning. Reducing misclassification costs. By clicking accept or continuing to use the site, you agree to the terms outlined in our. Cost-Time sensitive decision tree with missing values. Anytime induction of cost-sensitive trees. Tech. Academic Press, New York. High Performance Database Management for Large-Scale Applications. Automatica, 14, 465--471. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. Learn. 2007. In 16th European Conference on Machine Learning (ECML' 05). Boosting cost-sensitive trees. Anytime induction of low-cost, low-error classifiers: A sampling-based approach. Experiments in Induction. 506. Sometimes referred to as divide and conquer, this approach resembles a traditional if Yes then do A, if No, then do B flow chart. Bell Syst. Machine Learn. 6, 11, 1083--1090. Ferri, C., Flach, P., and Hernndez-Orallo, J. A new decision-tree-based classification algorithm, called SPRINT, is presented that removes all of the memory restrictions, and is fast and scalable, and designed to be easily parallelized, allowing many processors to work together to build a single consistent model. 283, 4, 82--87. Performance and results are compared of all algorithms and evaluation is done by already existing datasets and accuracy is more witnessed in case of SPRINT algorithm. Turney, P. D. 1995. 6, 231--250. Ting, K. and Zheng, Z. InProceedings of 17th European Conference on Machine Learning (ECML). 131--136. ACM Press New York. 1978. 18, 8, 1055--1067. Ting, K. M. 2002. 2329., Springer, 166--175. Inducing cost-sensitive non-linear decision trees. In Proceedings of the 16th International Conference on Machine Learning. 800--805. M. A. Bramer, Ed., Cambridge University Press. 392--395. https://dl.acm.org/doi/10.1145/2431211.2431215. Fan, W., Stolfo, S. J., Zhang, J., and Chan, P. K. 1999.
Quinlan, J. R. 1983.
MetaCost: A general method for making classifiers cost-sensitive. Bibliography.
Automatic model selection in cost-sensitive boosting. Mingers, J. 28, 3, 227--268. In Proceedings of The 21st Annual Conference on Neural Information Processing Systems (NIPS '07). 3721., Springer, 274--284. Ecol. Expert Syst. Frank, E. and Witten, I. Boosting trees for cost-sensitive classifications. W. Kim, R. Kohavi, J, J. Gehrke, and W. DuMouchel, Eds., 3. Inducing safer oblique trees without costs. A complete guide to the C4.5 system as implemented in C for the UNIX environment, which starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting. Quinlan, J. R. 1987.
Moret, S., Langford, W., and Margineantu, D. 2006. MPhil thesis, University of Salford. IEEE Trans. Lecture Notes in Computer Science, vol. Pruning decision trees with misclassification costs. Nilsson, N. J. An iterative method for multi-class cost-sensitive learning. AdaCost: Misclassification cost-sensitive boosting. Generating better decision trees. A comparative study of cost-sensitive boosting algorithms. An empirical study of Metacost using boosting algorithms. 4431, Springer, 1--10. 1999. Swets, J., Dawes, R., and Monahan, J. Lookahead and pathology in decision tree induction. Mach. Vadera, S. 2005b. Decision trees with minimal costs. A wrapper method for cost-sensitive learning via stratification. Qin, Z., Zhang, S., and Zhang, C. 2004. Machine Learn. IEEE Trans. Reduced-Error pruning with significance tests.
Art. This paper will discuss scalability of decision tree algorithm based on the selection of the attribute selection measure, and perform the comparative analysis of these measures to determine which measure should be used in which situation in order to increase the Scalability of the Decision Tree algorithm.
Model. Learning on FPGA, 09/03/2020 by Zhe Lin Margineantu, D. and Dietterich, T. 2003. Bureau Standards 12, 36--38. Hart, A. E. 1985. Zadrozny, B., Langford, J., and Abe, N. 2003a. Missing is useful: Missing values in cost-sensitive decision trees. Two--way Contingency Tables. In Proceedings of the 19th Machine Learning International Workshop then Conference. 2005. Hunt, E. B., Marin, J., and Stone, P. J. In Proceedings of the 9th International Conference on Data Warehousing and Knowledge Discovery. The use of background knowledge in decision tree induction. Learning to predict channel stability using biogeomorphic features. In Proceedings of the 10th European Conference on Machine Learning (ECML '98). Engin. Esmeir, S. and Markovitch, S. 2004. Chapman and Hall/CRC, London. C. E. Brodley, Ed., 257--264. Each resulting bucket of data would be pure in the sense that each data point would belong to the same class. A User's Guide to GENESIS v5.0. Pruning decision trees with misclassification costs. Sometimes these simplified trees are merely the byproduct after a more complex decision tree has been pruned of anomalies and outliers in the training data. 929. Cost-Sensitive classification with respect to waiting cost. Transition from Categorical to Survival Data. Knowl. Re-designing cost-sensitive decision tree learning. 191, 1, 47--57. Ting, K. 2000a. Learning class-sensitive active classifiers. In Proceedings of the 11th International Joint Conference on Artificial Intelligence (IJCAI '89). In Proceedings of the 17th International Conference on Machine Learning (ICML '00). InProceedings of the Budgeted Learning Workshop(ICML '10). Murthy, S. and Salzberg, S. 1995. Domingos, P. 1999. In Proceedings of the ACM International Conference on Machine Learning. In Proceedings of the 14th International Joint Conference on Artificial Intelligence. International Journal of ADVANCED AND APPLIED SCIENCES. It is shown that the data structure can be used to manage a set of n k-dimensional records or data items such that the records can be searched or updated in O(log2 n) + k time, which is optimal. J. Artif. The data mining technique used in this paper is classification in order to compare the data mining tools and the evaluation criterion used for the evaluation of tool is classification accuracy. Technol. A simple method for cost-sensitive learning. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '04). Issues in building a scalable classifier are discussed and the design of SLIQ, a new classifier that uses a novel pre-sorting technique in the tree-growth phase to enable classification of disk-resident datasets is presented. Zadrozny, B., Langford, J., and Abe, N. 2003b. A decision-theoretic generalization of on-line learning and an application to boosting, J. Comput. Lomax, S. and Vadera, S. 2011. 14, 3, 659--665. Lin, F. Y. and Mcclean, S. 2000. 1, 81--106. 2002. 1025--1033. Loglinear Models. 2000. Freund, Y. and Schapire, R. E. 1997. Discov. Breiman, L., Friedman J. H., Olsen R. A., and Stone C. J. An empirical comparison of voting classification algorithms: Bagging, boosting and variants. 1951. Data Engin. 3720. 2., Morgan Kaufmann, 973--978. Intell. Res. Lecture Notes in Computer Science, vol. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '06). A., Brodley, C. E., and Utgoff, P. E. 1994. Proceedings Seventh International Workshop on Research Issues in Data Engineering. The past decade has seen a significant interest on the problem of inducing decision trees that take account of costs of misclassification and costs of acquiring the features used for decision making. Learning Machines.
http://robotics.stanford.edu/~ronnyk/prune-long.ps.gz. J. Knowl. Dong, M. and Kothari, R. 2001. Addison Wesley. ACM Trans. 2005. In Proceedings of the 6th International Workshop on Machine Learning (ML '89). Lookahead-Based algorithms for anytime induction of decision trees. Improved boosting algorithms using confidence-rated predictions. 1966. Li, J., Li, X., and Yao, X. 148--156. Methods for Recommending Classification Algorithms (Extended Version), 09/16/2020 by Mrcio P. Basgalupp 1996. 2005. 2002. 36, 1-2, 105--139. Doctoral thesis, Oregon State University. In Proceedings of the 1st International Conference on Discovery Science. 37, 3, 297--336.
Estruch, V., Ferri, C., Hernndez-Orallo, J., and Ramrez-Quintana, m. j. In Research and Development in Expert Systems. Nnez, M. 1991.
Mach. Intell. Data mining classification algorithms: An overview, Comparative Analysis of Decision Tree Classification Algorithms, A Survey on Decision Tree Algorithms of Classification in Data Mining, A combination of decision tree learning and clustering for data classification, Comparative analysis of attribute selection measures used for attribute selection in decision tree induction, Survey on Decision Tree Classification algorithms for the Evaluation of Student Performance, Classification Techniques in Machine Learning: Applications and Issues, Performance of Data Mining Tools: A Case Study Based on Classification Algorithms and Datasets, Decision tree methods: applications for classification and prediction, Educational Data Mining by Using Neural Network, Generalization and decision tree induction: efficient classification in data mining, SLIQ: A Fast Scalable Classifier for Data Mining, SPRINT: A Scalable Parallel Classifier for Data Mining, Decision Tree Construction from Multidimensional Structured Data, Data-Driven Discovery of Quantitative Rules in Relational Databases, International Journal of Computer and Electrical Engineering. Data Engin. Modelling by shortest data description. Mease, D., Wyner, A. J., and Buja, A. Lecture Notes in Computer Science, vol. In Proceedings of the 3rd IEEE International Conference on Data Mining. Ni, A., Zhang, S., Yang, S., and Zhu, X. A data classification method which integrates attribute oriented induction, relevance analysis, and the induction of decision trees is proposed, which leads to efficient, high quality, multiple level classification of large amounts of data. Ling, C. X., Yang, Q., Wang, J., and Zhang, S. 2004. 33--42. System, 12/11/2020 by Zhe Lin For instance if a single node was able to find a set of splits and thresholds that partitioned the data uniformly into some collection of 'buckets', then we would have a model that perfectly classifies the data. In Proceedings of the 11th International Conference on Machine Learning.
Lozano, A.C. and Abe, N. 2008. 1998a. In Workshop de Mineria de Datos y Aprendizaje. Quinlan, J. R. 1986. 1989. Copyright 2022 ACM, Inc. Abe, N., Zadrozny, B., and Langford, J. IEEE Trans. 16, 9, 888--893. Sci. Like all decision trees, this algorithm includes a root node, branches, and leaf nodes. Discovering rules by induction from large collections of examples. 383--386. Davis, J. V., Jungwoo, H., and Rossbach, C. J. Winston, P. H. 1993. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.27.1102. 983--990.
Ling, C., Sheng, V., and Yang, Q. Sci., Engin.
Esmeir, S. and Markovitch, S. 2008. In Proceedings of the 16th International Joint Conference on Artificial Intelligence (IJCAI99). In Proceedings of the International Conference on Computational Science (ICCS '02). http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.46.2272. Zhang, S., Zhu, X., Zhang, J., and Zhang, C. 2007. Afifi, A. 4, 1080--1085. 139, 2, 137--174. Vol. 2007. Morgan Kaufman, San Mateo, CA. Small nets and short paths: Optimizing neural computation. Zhang, S., Qin, Z., Ling, C., and Sheng, S. 2005.
Meir, R. and Rtsch, g. 2003. View 2 excerpts, references background and methods. 23, 5, 369--378. Learn.
We use cookies to ensure that we give you the best experience on our website. Inductive knowledge acquisition: A case study.
Lecture Notes in Computer Science, vol. 2012 International Conference on Radar, Communication and Computing (ICRCC). 2002. Cost-Sensitive concept learning of sensor use in approach and recognition. Hybrid cost-sensitive decision tree. Knowl. 97--105. Studies 27, 221--234. Res. Freund, Y. and Schapire, R. E. 1996. 22, 4, 206--221. Cost-Sensitive classification with genetic programming. J. Mach. A new cost-sensitive decision tree with missing values. In Proceedings of the 13th International Machine Learning Workshop then Conference. 2, 369--409. Bagging predictors. Quinlan, J. R., Compton, P. J., Horn, K. A., and Lazarus, L. 1987. Impurity is essentially measures how well each node with its corresponding splits and thresholds separates the data. Genetic Algorithms + Data Structures = Evolution Programs 3rd Ed.
1--8. This paper examines the various types of classification algorithms in Data Mining, their applications and categorically states the strengths and limitations of each type. Learning efficient classification procedures and their application to chess end games. Algorithms used to develop decision trees are introduced and the SPSS and SAS programs that can be used to visualize tree structure are described, including CART, C4.5, CHAID, and QUEST. 1998b.
2006b.
In Proceedings of the IEEE Congress on Evolutionary Computation. Vol 2, 1401--1406. 1994.
Lecture Notes in Computer Science, vol. McGraw-Hill, New York. Experiments with a new boosting algorithm. Logistic Regression Models. J. Artif. Data Engin. http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.7.7947. Experience in the use of an inductive system in knowledge engineering. 1532., Springer, 244--255. Learn. A Brief Introduction to Boosting. An introduction to boosting and leveraging. 2005. In Proceedings of the 10th European Conference on Machine Learning. Chapman & Hall, London. 18, Hard-ODT: Hardware-Friendly Online Decision Tree Learning Algorithm and Zhang, S. 2010. Cost-Sensitive decision tree learning for forensic classification. This alert has been successfully added and will be sent to: You will be notified whenever a record that you have chosen has been cited. 1998. McGraw-Hill, New York. 2114--2121. Grefenstette, J. J. Check if you have access through your login credentials or your institution to get full access on this article. Learn. Fuzzy Syst. Mach. Ting, K. M. and Zheng, Z. Evolutionary induction of decision trees for misclassification cost minimization. Intell. Springer. Schapire, R. E. 1999.
Omielan, A. 17, 12, 1689--1693. Shannon, C. E. 1948. Simplifying decision trees. Esmeir, S. and Markovitch, S. 2007. Murthy, S., Kasif, S., and Salzberg, S. 1994. 0, Fair Forests: Regularized Tree Induction to Minimize Model Bias, 12/21/2017 by Edward Raff The foundations of cost-sensitive learning. The mathematical theory of communication. Eds., Springer, 119--184. J. Artif. Ferri-Ramrez, C., Hernndez, J., and Ramirez, M. J. C4.5: Programs for Machine Learning. Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06). G. I. Webb and X. Yu, Eds., Lecture Notes in Artificial Intelligence, vol. The goal of this study is to provide a comprehensive review of different classification techniques in machine learning and will be helpful for both academia and new comers in the field of machine learning to further strengthen the basis of classification methods. Esmeir, S. and Markovitch, S. 2011. Mach. ACM, 155--164. Breiman, L. 1996. Engin. 4, 227--243. Kretowski, M. and Grzes, M. 2007. A new SPRINT decision tree algorithm is represented which will be used to solve the problems of classification in educational data system and is fast and scalable than others because it can be implemented in both serial and parallel fashion good data replacement and load balancing. Or after a larger decision tree has been optimized to reduce the number of leaves, or its cost complexity. Either approach often leaves the tree with a straightforward decision path with only a few options available at any given layer. An empirical comparison of cost-sensitive decision tree induction algorithms. Therefore we want to maximize the resulting purity after each node decision (equivalently we wish to minimize the impurity). CSNL: A cost-sensitive non-linear decision tree algorithm.
Knowl. J. Springer, 139--147. Index. Norton, S. W. 1989. 217--225. http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:Reducing+&q=intitle:Reducing++misclassification+costs#0. Various algorithms of Decision tree (ID3, C4.5, CART), their characteristic, challenges, advantage and disadvantage, are focused on. In Proceedings of the 2nd European Symposium on Principles of Data Mining and Knowledge Discovery. 4798, 447--459. Methods for cost-sensitive learning. Reducing misclassification costs. By clicking accept or continuing to use the site, you agree to the terms outlined in our. Cost-Time sensitive decision tree with missing values. Anytime induction of cost-sensitive trees. Tech. Academic Press, New York. High Performance Database Management for Large-Scale Applications. Automatica, 14, 465--471. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. Learn. 2007. In 16th European Conference on Machine Learning (ECML' 05). Boosting cost-sensitive trees. Anytime induction of low-cost, low-error classifiers: A sampling-based approach. Experiments in Induction. 506. Sometimes referred to as divide and conquer, this approach resembles a traditional if Yes then do A, if No, then do B flow chart. Bell Syst. Machine Learn. 6, 11, 1083--1090. Ferri, C., Flach, P., and Hernndez-Orallo, J. A new decision-tree-based classification algorithm, called SPRINT, is presented that removes all of the memory restrictions, and is fast and scalable, and designed to be easily parallelized, allowing many processors to work together to build a single consistent model. 283, 4, 82--87. Performance and results are compared of all algorithms and evaluation is done by already existing datasets and accuracy is more witnessed in case of SPRINT algorithm. Turney, P. D. 1995. 6, 231--250. Ting, K. and Zheng, Z. InProceedings of 17th European Conference on Machine Learning (ECML). 131--136. ACM Press New York. 1978. 18, 8, 1055--1067. Ting, K. M. 2002. 2329., Springer, 166--175. Inducing cost-sensitive non-linear decision trees. In Proceedings of the 16th International Conference on Machine Learning. 800--805. M. A. Bramer, Ed., Cambridge University Press. 392--395. https://dl.acm.org/doi/10.1145/2431211.2431215. Fan, W., Stolfo, S. J., Zhang, J., and Chan, P. K. 1999.
Quinlan, J. R. 1983.
MetaCost: A general method for making classifiers cost-sensitive. Bibliography.
Automatic model selection in cost-sensitive boosting. Mingers, J. 28, 3, 227--268. In Proceedings of The 21st Annual Conference on Neural Information Processing Systems (NIPS '07). 3721., Springer, 274--284. Ecol. Expert Syst. Frank, E. and Witten, I. Boosting trees for cost-sensitive classifications. W. Kim, R. Kohavi, J, J. Gehrke, and W. DuMouchel, Eds., 3. Inducing safer oblique trees without costs. A complete guide to the C4.5 system as implemented in C for the UNIX environment, which starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting. Quinlan, J. R. 1987.
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