Title A two-stage evolutionary algorithm based on sensitivity and accuracy for multi-class problems
Authors Gutiérrez P.A., Hervás-Martínez C., MARTÍNEZ ESTUDILLO, FRANCISCO JOSÉ, CARBONERO RUZ, MARIANO, CARBONERO RUZ, MARIANO, MARTÍNEZ ESTUDILLO, FRANCISCO JOSÉ
External publication No
Means Inf Sci
Scope Article
Nature Científica
JCR Quartile 1
SJR Quartile 1
JCR Impact 3.64300
SJR Impact 2.12700
Area International
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-84859099327&doi=10.1016%2fj.ins.2012.02.012&partnerID=40&md5=2102af83f0e6cfab02f6627dc47b253a
Publication date 01/01/2012
ISI 000303093100002
Scopus Id 2-s2.0-84859099327
DOI 10.1016/j.ins.2012.02.012
Abstract The machine learning community has traditionally used correct classification rates or accuracy (C) values to measure classifier performance and has generally avoided presenting classification levels of each class in the results, especially for problems with more than two classes. C values alone are insufficient because they cannot capture the myriad of contributing factors that differentiate the performance of two different classifiers. Receiver Operating Characteristic (ROC) analysis is an alternative to solve these difficulties, but it can only be used for two-class problems. For this reason, this paper proposes a new approach for analysing classifiers based on two measures: C and sensitivity (S) (i.e., the minimum of accuracies obtained for each class). These measures are optimised through a two-stage evolutionary process. It was conducted by applying two sequential fitness functions in the evolutionary process, including entropy (E) for the first stage and a new fitness function, area (A), for the second stage. By using these fitness functions, the C level was optimised in the first stage, and the S value of the classifier was generally improved without significantly reducing C in the second stage. This two-stage approach improved S values in the generalisation set (whereas an evolutionary algorithm (EA) based only on the S measure obtains worse S levels) and obtained both high C values and good classification levels for each class. The methodology was applied to solve 16 benchmark classification problems and two complex real-world problems in analytical chemistry and predictive microbiology. It obtained promising results when compared to other competitive multi-class classification algorithms and a multi-objective alternative based on E and S. © 2012 Elsevier Inc. All rights reserved.
Keywords Accuracy; Imbalanced datasets; Multi-class; Sensitivity; Two stage; Classifiers; Health; Learning algorithms; Learning systems; Classification (of information)
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