A robust approach to model-based classification based on trimming and constraints

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Title: A robust approach to model-based classification based on trimming and constraints
Authors: Cappozzo, AndreaGreselin, FrancescaMurphy, Thomas Brendan
Permanent link: http://hdl.handle.net/10197/11026
Date: 14-Aug-2019
Online since: 2019-08-26T07:01:50Z
Abstract: In a standard classification framework a set of trustworthy learning data are employed to build a decision rule, with the final aim of classifying unlabelled units belonging to the test set. Therefore, unreliable labelled observations, namely outliers and data with incorrect labels, can strongly undermine the classifier performance, especially if the training size is small. The present work introduces a robust modification to the Model-Based Classification framework, employing impartial trimming and constraints on the ratio between the maximum and the minimum eigenvalue of the group scatter matrices. The proposed method effectively handles noise presence in both response and exploratory variables, providing reliable classification even when dealing with contaminated datasets. A robust information criterion is proposed for model selection. Experiments on real and simulated data, artificially adulterated, are provided to underline the benefits of the proposed method.
Funding Details: Science Foundation Ireland
Type of material: Journal Article
Publisher: Springer
Journal: Advances in Data Analysis and Classification
Start page: 1
End page: 28
Copyright (published version): 2019 Springer
Keywords: Model-based classificationLabel noiseOutliers detectionImpartial trimmingEigenvalues restrictionsRobust estimation
DOI: 10.1007/s11634-019-00371-w
Language: en
Status of Item: Peer reviewed
Appears in Collections:Insight Research Collection

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