Linear Classifier Combination via Multiple Potential Functions

10/02/2020
by   Pawel Trajdos, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
05/23/2019

Combination of linear classifiers using score function -- analysis of possible combination strategies

In this work, we addressed the issue of combining linear classifiers usi...
research
09/16/2021

Probability-driven scoring functions in combining linear classifiers

Although linear classifiers are one of the oldest methods in machine lea...
research
03/15/2017

Ensemble of Neural Classifiers for Scoring Knowledge Base Triples

This paper describes our approach for the triple scoring task at the WSD...
research
11/22/2019

Responsible Scoring Mechanisms Through Function Sampling

Human decision-makers often receive assistance from data-driven algorith...
research
10/25/2021

Least Square Calibration for Peer Review

Peer review systems such as conference paper review often suffer from th...
research
08/02/2021

Multiple Classifiers Based Maximum Classifier Discrepancy for Unsupervised Domain Adaptation

Adversarial training based on the maximum classifier discrepancy between...
research
07/28/2014

Entropic one-class classifiers

The one-class classification problem is a well-known research endeavor i...

Please sign up or login with your details

Forgot password? Click here to reset