Linear Ordering Problem based Classifier Chain using Genetic Algorithm for multi-label classification

Abstract

One of the most challenging tasks in multi-label classification is to identify label interdependence. Classifier Chain is the most prevalent method that utilizes label interdependence for improving classification accuracy as it requires only the number of classifiers equal to the number of labels. It uses a random sequence of labels. However, the order of labels in these sequences affects the classification performance. Nevertheless, despite many proposals in the literature, deciding the order in which these classifiers provide optimum results is a challenge to date. This paper proposes two methods for the ordering problem of the Classifier chain. The first proposed method termed as Linear Ordering Problem based Classifier Chain (LOP-CC) finds the chain order by considering it as a Linear Ordering Problem (LOP). The LOP utilizes a matrix and finds the optimal permutation of rows and corresponding columns that maximizes the sum of all the elements in the upper triangular matrix. This paper utilizes pairwise conditional entropy for creating the matrix to be used with the LOP and solves it using the Genetic Algorithm. It also proposes an extension to LOP-CC method termed as Linear Ordering Problem based partial Classifier Chain (LOP-pCC). It uses the same order of labels as LOP-CC. However, as opposed to LOP-CC, it utilizes partial sequences in the classifier chain rather than a full sequence. Experimentation performed on ten benchmark datasets consisting of a varying number of labels using different performance metrics demonstrates the proposed methods’ effectiveness compared to the other state-of-the-art classifier chain models.