Linear Classifier Combination via Multiple Potential Functions

TL;DR

This paper introduces a new scoring function for linear classifiers that considers object distances to decision boundaries and class centroids, improving ensemble classification performance.

Contribution

It proposes a novel, unified scoring function for linear classifiers that incorporates spatial information, enhancing ensemble methods.

Findings

Effective on multiple datasets

Outperforms other ensemble algorithms

Validated with statistical analysis

Abstract

A vital aspect of the classification based model construction process is the calibration of the scoring function. One of the weaknesses of the calibration process is that it does not take into account the information about the relative positions of the recognized objects in the feature space. To alleviate this limitation, in this paper, we propose a novel concept of calculating a scoring function based on the distance of the object from the decision boundary and its distance to the class centroid. An important property is that the proposed score function has the same nature for all linear base classifiers, which means that outputs of these classifiers are equally represented and have the same meaning. The proposed approach is compared with other ensemble algorithms and experiments on multiple Keel datasets demonstrate the effectiveness of our method. To discuss the results of our experiments, we use multiple classification performance measures and statistical analysis.