Semantic Channel and Shannon's Channel Mutually Match for Multi-Label Classification

TL;DR

This paper introduces a novel multi-label classification method based on matching semantic and Shannon channels, utilizing a Channel Matching algorithm that improves label prediction by aligning semantic information with probabilistic channels.

Contribution

It proposes a new framework for multi-label classification using semantic and Shannon channel matching, along with the Channel Matching algorithm that enhances label prediction and handles class imbalance.

Findings

The CM algorithm adheres to maximum semantic information criterion.

It effectively handles class imbalance by adapting to source changes.

The method improves multi-label classification accuracy and robustness.

Abstract

A group of transition probability functions form a Shannon's channel whereas a group of truth functions form a semantic channel. Label learning is to let semantic channels match Shannon's channels and label selection is to let Shannon's channels match semantic channels. The Channel Matching (CM) algorithm is provided for multi-label classification. This algorithm adheres to maximum semantic information criterion which is compatible with maximum likelihood criterion and regularized least squares criterion. If samples are very large, we can directly convert Shannon's channels into semantic channels by the third kind of Bayes' theorem; otherwise, we can train truth functions with parameters by sampling distributions. A label may be a Boolean function of some atomic labels. For simplifying learning, we may only obtain the truth functions of some atomic label. For a given label, instances are divided into three kinds (positive, negative, and unclear) instead of two kinds as in popular studies so that the problem with binary relevance is avoided. For each instance, the classifier selects a compound label with most semantic information or richest connotation. As a predictive model, the semantic channel does not change with the prior probability distribution (source) of instances. It still works when the source is changed. The classifier changes with the source, and hence can overcome class-imbalance problem. It is shown that the old population's increasing will change the classifier for label "Old" and has been impelling the semantic evolution of "Old". The CM iteration algorithm for unseen instance classification is introduced.