C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
SSVM: A Smooth Support Vector Machine for Classification
Computational Optimization and Applications
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Dynamic Discretization of Continuous Attributes
IBERAMIA '98 Proceedings of the 6th Ibero-American Conference on AI: Progress in Artificial Intelligence
Linear Machine Decision Trees
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
A system for induction of oblique decision trees
Journal of Artificial Intelligence Research
OC1: randomized induction of oblique decision trees
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Multivariate decision trees using linear discriminants and tabu search
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A New Incremental Algorithm for Induction of Multivariate Decision Trees for Large Datasets
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
Building fast decision trees from large training sets
Intelligent Data Analysis
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In the task of classification, most learning methods are suitable only for certain data types. For the hybrid dataset consists of nominal and numeric attributes, to apply the learning algorithms, some attributes must be transformed into the appropriate types. This procedure could damage the nature of dataset. We propose a model tree approach to integrate several characteristically different learning methods to solve the classification problem. We employ the decision tree as the classification framework and incorporate support vector machines into the tree construction process. This design removes the discretization procedure usually necessary for tree construction while decision tree induction itself can deal with nominal attributes which may not be handled well by e.g., SVM methods. Experiments show that our purposed method has better performance than that of other competing learning methods.