Abstract
In the machine learning jargon, multi-label classification refers to a task where multiple mutually non-exclusive class labels are assigned to a single instance. Generally, the lack of sufficient labeled training data demanded by a classification task is met by an approach known as semi-supervised learning. This type of learning extracts the decision rules of classification by utilizing both labeled and unlabeled data. Regarding multi-label data, however, current semi-supervised learning methods are unable to classify them accurately. Therefore, with the goal of generalizing the state-of-the-art semi-supervised approaches to multi-label data, this paper proposes a novel two-stage method for multi-label semi-supervised classification. The first stage determines the label(s) of the unlabeled training data by means of a smooth graph constructed using the manifold regularization. In the second stage, thanks to the capability of the twin support vector machine to relax the requirement that hyperplanes should be parallel in classical SVM, we employ it to establish a multi-label classifier called LP-MLTSVM. In the experiments, this classifier is applied on benchmark datasets. The simulation results substantiate that compared to the existing multi-label classification algorithms, LP-MLTSVM shows superior performance in terms of the Hamming loss, average precision, coverage, ranking loss, and one-error metrics.
Highlights
C LASSIFICATION is a well-known task in the field of machine learning
We compare it with MLTSVM, Rnak-Support vector machine (SVM), BPMLL and SS-MLLSTSVM using synthetic and real datasets
This paper aimed to leverage a large number of unlabeled data along with a limited number of labeled data for increasing classifier’s precision
Summary
C LASSIFICATION is a well-known task in the field of machine learning. Traditionally, machine learning techniques are divided into supervised, unsupervised and semi-supervised.In supervised learning, one or more labels are assigned to each given data point by the intervention of a supervisor [1]. Support vector machine (SVM) [4], twin support vector machine (TWSVM) [5] and neural networks [6] are well-known examples of supervised learning. These types of learning have been used in a wide range of applications like pattern recognition [7] and text categorization [8]. Spam email filtering is a single-label classification problem where each instance has a single-label In this problem, once the classifier learns the features of a spam email, it will be able to distinguish spam from non-spam emails [9]
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