Combining Linear Classifiers Using Probability-Based Potential Functions

Pawel Trajdos,Robert Burduk

doi:10.1109/access.2020.3038341

Abstract

The score function can be used as a measure for evaluating predicted probabilities of the classification models. In multiple classifiers systems, one of the problems is the diversity of the way of determining the scoring function of individual base classifiers. To alleviate this limitation, in this article, we propose a novel concept of calculating a scoring function defined by the probability-based potential function. The proposed potential functions take into account the distance of the recognized object from the decision boundary as well as a prior probability of the class labels. The proposed score function has the same nature for all linear base classifiers, which defined the multiple classifiers model. Additionally, the proposed method is compared with other ensemble algorithms based on homogeneous linear base classifiers. The experiments on seventy databases demonstrate the effectiveness of our method. To discuss the results of our experiments, we use multiple classification performance measures dedicated to standard and imbalanced datasets. The statistical analysis of the experiments is also performed.

Highlights

The idea of building an ensemble of classifiers (EoC) is to compose a single strong model from the pool of weak or different ones
EoCs improve the possibilities of individual base models by building a more stable and accurate complex model [1]
The real-world classification problems solved with EoC was already mentioned in paper [2] because EoC increase the performance of individual classification models

Summary

Introduction

The idea of building an ensemble of classifiers (EoC) is to compose a single strong model from the pool of weak or different ones. EoCs improve the possibilities of individual base models (base classifiers) by building a more stable and accurate complex model [1]. The real-world classification problems solved with EoC was already mentioned in paper [2] because EoC increase the performance of individual classification models. EoC have been applied in many industrial fields like: the optimal stacking ensemble for remaining useful life estimation was proposed in [5], classification of cutting tools [6] or in the in-line detection of surface defects on glass substrates of thin-film transistor liquid crystal displays [7]. The EoC classifiers play an important role in the multi-label classification problems

Methods

Results

Conclusion