Abstract
Multi-label learning with emerging new labels is a practical problem that occurs in data streams and has become an important new research issue in the area of machine learning. However, existing models for dealing with this problem require high learning computational times, and there still exists a lack of research. Based on these issues, this paper presents an incremental kernel extreme learning machine for multi-label learning with emerging new labels, consisting of two parts: a novelty detector; and a multi-label classifier. The detector with free-user-setting threshold parameters was developed to identify instances with new labels. A new incremental multi-label classifier and its improved version were developed to predict a label set for each instance, which can add output units incrementally and update themselves in unlabeled instances. Comprehensive evaluations of the proposed method were carried out on the problems of multi-label classification with emerging new labels compared to comparative algorithms, which revealed the promising performance of the proposed method.
Highlights
Multi-label learning problems have become a key topic in the research area of data mining and have gained increased attention in the field of machine learning [1], [2]
We proposed a new incremental multi-label classifier based upon the improved kernel extreme learning machine (KELM), which incrementally increases the number of output units and updates itself by using unlabeled instances
The remainder of this paper is organized as follows: in Section II, we provide a brief review of related works; Section III explains the problem formulation of multi-label learning with emerging new labels; and Section IV outlines our proposed method, including novelty detection and multilabel classifiers
Summary
Multi-label learning problems have become a key topic in the research area of data mining and have gained increased attention in the field of machine learning [1], [2]. Multi-label learning tasks involve finding the relation between each sample xi = [xi,1, . Xi,n] and a set of labels yi = [yi,1, . Yi,m], where yi,j is the jth label of yi. In movie categorization tasks, the genres of one movie can belong to comedy, romance, and fantasy. Several approaches have been proposed to handle multi-label problems; such as the back-propagation (BP) method [3], support vector machine (SVM) [4], k-nearest neighbors (kNN) [5], classifier chain (CC) [6], and decision
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