Unified Graph-Based Missing Label Propagation Method for Multilabel Text Classification

Adil Yaseen Taha,Masri Ayob,Abdul Hadi Abd Rahman,Sabrina Tiun,Ali Sabah Abdulameer

doi:10.3390/sym14020286

Abstract

In multilabel classification, each sample can be allocated to multiple class labels at the same time. However, one of the prominent problems of multilabel classification is missing labels (incomplete labels) in multilabel text. The multilabel classification performance is reduced significantly with the presence of missing labels. In order to address the incomplete or missing label problem, this study proposes two methods: an aggregated feature and label graph-based missing label handling method (GB-AS), and a unified graph-based missing label propagation method (UG-MLP). GB-AS is used to obtain an initial label matrix based on the similarity of both document levels: feature-based weighting representation and label-based weighting representation. On the other hand, UG-MLP is introduced to construct a mixed graph that combines GB-AS and label correlations into a single groundwork. A high-order label correlation is learned from the incomplete training data and applied to supplement the missing label matrix, which guides the creation of multilabel classification models. The combination of the mixed graphs by UG-MLP is aimed to obtain the benefits of both graphs to increase the classification performance. To evaluate UG-MLP, the metrics of precision, recall and F-measure were used on three benchmark datasets, namely, the Reuters-21578, Bibtex and Enron datasets. The experimental results show that UG-MLP outperformed GB-AS as well as other state-of-the-art approaches. Therefore, we can infer from the findings that by plotting a unified graph based on joining aggregated feature and label weightings together with the label correlation, the performance of multilabel classification can be improved.

Highlights

In multilabel learning, each label is connected with one or more labels simultaneously
The results obtained (F-measure) for DMMC-EFS after label recovery with one of the four missing label handling methods are shown in Table 2 and Figure 4
Based on the results of this experiment, almost the same observations were made: The incompleteness of class labels significantly influences the performance of multilabel classifiers, and these approaches to modeling missing labels offer a better performance than DMMC-EFS in most cases

Summary

Introduction

Each label is connected with one or more labels simultaneously. The following are open problems: high dimensionality, feature and label correlations and missing labels in multilabel classification [1]. Handling high dimensionality and feature correlations in multilabel learning may not effectively work if it does not consider the missing label problem (incomplete and noisy label space). Most contemporary approaches treat this problem as a supervised weak-label learning problem, assuming that there are enough partially labeled examples available [2,3,4]. Collecting or annotating such instances, on the other hand, is costly and time consuming. The label sets of objects sharing the same cluster are strongly connected, whereas label sets of other clusters are loosely correlated [5]

Methods

Results

Discussion

Conclusion