Multithreshold Entropy Linear Classifier: Theory and applications
作者：
Highlights：
• We propose a new entropy based multithreshold linear classifier with an adaptive kernel density estimation.
• Proposed classifier maximizes multiple margins, while being conceptually similar in nature to SVM.
• This method gives good classification results and is especially designed for unbalanced datasets.
• It achieves significantly better results than SVM as part of an expert system designed for drug discovery.
• Resulting model provides insight into the internal data geometry and can detect multiple clusters.
摘要
•We propose a new entropy based multithreshold linear classifier with an adaptive kernel density estimation.•Proposed classifier maximizes multiple margins, while being conceptually similar in nature to SVM.•This method gives good classification results and is especially designed for unbalanced datasets.•It achieves significantly better results than SVM as part of an expert system designed for drug discovery.•Resulting model provides insight into the internal data geometry and can detect multiple clusters.
论文关键词：Classification,Renyi’s entropy,Density estimation,Multithreshold classifier
论文评审过程：Available online 18 March 2015.
论文官网地址：https://doi.org/10.1016/j.eswa.2015.03.007
