An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Data mining: concepts and techniques
Data mining: concepts and techniques
Machine Learning
Computers and Operations Research
Mining customer product ratings for personalized marketing
Decision Support Systems - Special issue: Web data mining
Intelligent data analysis
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
A hybrid model for exchange rate prediction
Decision Support Systems
A machine learning approach to web page filtering using content and structure analysis
Decision Support Systems
A Multi-criteria Convex Quadratic Programming model for credit data analysis
Decision Support Systems
A system for induction of oblique decision trees
Journal of Artificial Intelligence Research
A scalable decision tree system and its application in pattern recognition and intrusion detection
Decision Support Systems
Top-down induction of decision trees classifiers - a survey
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Data-Mining-Driven Neighborhood Search
INFORMS Journal on Computing
Review: Supervised classification and mathematical optimization
Computers and Operations Research
A use of DEA-DA to measure importance of R&D expenditure in Japanese information technology industry
Decision Support Systems
Hi-index | 0.00 |
Two-group classification is a key task in decision making and data mining applications. We introduce two new mixed integer programming formulations that make use of multiple separating hyperplanes. They represent a generalization of previous piecewise-linear models that embed rules having the form of hyperplanes, which are used to successively separate the two groups. In fact, the classifiers obtained are particular types of decision trees which are allowed to grow in depth and not in width. Computational results show that our models achieve better classification accuracy in less time than previous approaches.