A Novel ECOC Algorithm with Centroid Distance Based Soft Coding Scheme
Kaijie Feng, Kunhong Liu, Beizhan Wang

TL;DR
This paper introduces CSECOC, a novel ECOC algorithm employing a centroid distance-based soft coding scheme that captures class distribution tendencies, leading to improved classification accuracy with small ensembles.
Contribution
It proposes the first soft coding scheme for ECOC, utilizing class distribution tendencies and regression classifiers, enhancing classification performance over traditional hard coding methods.
Findings
Achieves comparable or better accuracy than state-of-the-art ECOC algorithms.
Uses regression as base learners instead of classifiers.
Demonstrates effectiveness on five UCI datasets.
Abstract
In ECOC framework, the ternary coding strategy is widely deployed in coding process. It relabels classes with {"-1,0,1" }, where -1/1 means to assign the corresponding classes to the negative/positive group, and label 0 leads to ignore the corresponding classes in the training process. However, the application of hard labels may lose some information about the tendency of class distributions. Instead, we propose a Centroid distance-based Soft coding scheme to indicate such tendency, named as CSECOC. In our algorithm, Sequential Forward Floating Selection (SFFS) is applied to search an optimal class assignment by minimizing the ratio of intra-group and inter-group distance. In this way, a hard coding matrix is generated initially. Then we propose a measure, named as coverage, to describe the probability of a sample in a class falling to a correct group. The coverage of a class a group…
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Taxonomy
TopicsFace and Expression Recognition · Machine Learning and Algorithms · Imbalanced Data Classification Techniques
