C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
Support vector machines with different norms: motivation, formulations and results
Pattern Recognition Letters
Machine Learning
Everything old is new again: a fresh look at historical approaches in machine learning
Everything old is new again: a fresh look at historical approaches in machine learning
A computational TW3 classifier for skeletal maturity assessment: a computing with words approach
Journal of Biomedical Informatics
Incremental training of support vector machines using hyperspheres
Pattern Recognition Letters
Artificial Intelligence in Medicine
Induction of multiple criteria optimal classification rules for biological and medical data
Computers in Biology and Medicine
Controlling inventory by combining ABC analysis and fuzzy classification
Computers and Industrial Engineering
Sphere-structured support vector machines for multi-class pattern recognition
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Frontiers of Computer Science in China
Peer-estimation for multiple criteria ABC inventory classification
Computers and Operations Research
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The classification of biological and medical datasets is essential to humanity. This study proposes a hyper ellipse method based on mixed integer nonlinear program for classifying datasets. A linearization technique with a number of piecewise line segments is used to treat nonlinear constraints, which aims to obtain an approximate optimal solution. Numerical examples are presented to demonstrate the efficacy of the proposed method.