University Of Tasmania

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Detecting potential labeling errors for bioinformatics by multiple voting

journal contribution
posted on 2023-05-19, 14:02 authored by Guan, D, Yuan, W, Ma, T, Lee, S
Classification techniques are important in bioinformatics analysis as they can separate various bioinformatical data into distinct groups. To obtain good classifiers, accurate labeling of the training data is required. However labeling in practical bioinformatics applications might be erroneous due to various reasons. To identify those mislabeled data, an ensemble learning based scheme, single-voting has been widely used. It generates multiple classifiers and makes use of their voting to detect mislabeled data. Single-voting scheme mainly consists of two components: data partitioning component to generate multiple classifiers, and mislabeled detection component to identify mislabeled data. Existing works in this field mainly focus on mislabeled detection part and neglect data partitioning. However, our analysis shows that data partitioning plays an important role in single-voting scheme. This analysis helps us proposing a novel multiple-voting scheme. It is superior to traditional single-voting by reducing the unreliable influence from data partitioning. Empirical and theoretical evaluations on a set of bioinformatics datasets illustrate the utility of our proposed scheme.


Publication title

Knowledge-Based Systems








School of Engineering


Elsevier Science Bv

Place of publication

Po Box 211, Amsterdam, Netherlands, 1000 Ae

Rights statement

Copyright 2014 Elsevier B.V.

Repository Status

  • Restricted

Socio-economic Objectives

Information systems, technologies and services not elsewhere classified