M-ary Rank Classifier Combination: A Binary Linear Programming Problem.

Vincent Vigneron,Hichem Maaref

doi:10.3390/e21050440

Abstract

The goal of classifier combination can be briefly stated as combining the decisions of individual classifiers to obtain a better classifier. In this paper, we propose a method based on the combination of weak rank classifiers because rankings contain more information than unique choices for a many-class problem. The problem of combining the decisions of more than one classifier with raw outputs in the form of candidate class rankings is considered and formulated as a general discrete optimization problem with an objective function based on the distance between the data and the consensus decision. This formulation uses certain performance statistics about the joint behavior of the ensemble of classifiers. Assuming that each classifier produces a ranking list of classes, an initial approach leads to a binary linear programming problem with a simple and global optimum solution. The consensus function can be considered as a mapping from a set of individual rankings to a combined ranking, leading to the most relevant decision. We also propose an information measure that quantifies the degree of consensus between the classifiers to assess the strength of the combination rule that is used. It is easy to implement and does not require any training. The main conclusion is that the classification rate is strongly improved by combining rank classifiers globally. The proposed algorithm is tested on real cytology image data to detect cervical cancer.

Highlights

Using a single classifier has shown limitations in achieving satisfactory recognition performance, and this leads us to use multiple classifiers, which is a common practice in machine learning.Classifier combination has been studied in many disciplines such as the social sciences, sensor fusion, pattern recognition, etc
Lowercase letters refer to individual elements in a vector whose position in the vector is indicated by the last subscript
Xij refers to the jth element of vector xi . p(Ci ) is the a priori probability of the random value

Summary

Introduction

Using a single classifier has shown limitations in achieving satisfactory recognition performance, and this leads us to use multiple classifiers, which is a common practice in machine learning. Classifier combination has been studied in many disciplines such as the social sciences, sensor fusion, pattern recognition, etc. Schapire [1] proved that a strong classifier can be generated by combining weak classifiers. It has been accepted as an effective method to improve classification performances. Many examples of ensemble classifier systems can be found in process engineering or medicine. For a survey of the issues and approaches on classifier combination, readers are referred to Woźniak [2] and

Methods

Results

Conclusion