We develop a novel probabilistic ensemble framework for multi-label classification that is based on the mixtures-of-experts architecture. In this framework, we combine multi-label classification models in the classifier chains family that decompose the class posterior distribution P(Y1, …, Yd |X) using a product of posterior distributions over components of the output space. Our approach captur...

Artificial intelligence techniques aimed at more naturally simulating human comprehension fit the paradigm of multi-label classification. Generally, an enormous amount of high-quality multi-label data is needed to form a multi-label classifier. The creation of such datasets is usually expensive and timeconsuming. A lower cost way to obtain multi-label datasets for use with such comprehension–si...

Multi-label learning deals with the problems when each instance can be assigned to multiple classes simultaneously, which are ubiquitous in real-world learning tasks. In this paper, we propose a new multilabel learning method, which is able to exploit unlabeled data to obtain an effective model for assigning appropriate multiple labels to instances. The proposed method is called T (TRansduct...

Introduction: Applying of a new indicator in food packaging can be effective to inform consumers about the freshness and quality of the products. Materials and Methods: In the current study, a new milk freshness label was investigated containing beetroot color and multi layers of polystyrene. The label characteristics were investigated by estimating color number, release test, and scanning ele...

With the diversification of the TC task, to construct multi-label classifier is often more in line with the needs of practical applications. However, in a multi-label classification task, each document often corresponds to more than one class label. In this chapter, we will construct a compound classification framework which may transform a multi-label classification task into several single la...

This paper investigates hierarchy extraction from results of multi-label classification (MC). MC deals with instances labeled by multiple classes rather than just one, and the classes are often hierarchically organized. Usually multi-label classifiers rely on a predefined class hierarchy. A much less investigated approach is to suppose that the hierarchy is unknown and to infer it automatically...

Multi-label classification methods are increasingly required by modern applications, such as protein function classification, music categorization, and semantic scene classification. This article introduces the task of multi-label classification, organizes the sparse related literature into a structured presentation and performs comparative experimental results of certain multi-label classifica...

There has been a lot of research targeting text classification. Many of them focus on a particular characteristic of text data multi-labelity. This arises due to the fact that a document may be associated with multiple classes at the same time. The consequence of such a characteristic is the low performance of traditional binary or multi-class classification techniques on multi-label text data....

Multi-label classification has attracted an increasing amount of attention in recent years. To this end, many algorithms have been developed to classify multi-label data in an effective manner. However, they usually do not consider the pairwise relations indicated by sample labels, which actually play important roles in multi-label classification. Inspired by this, we naturally extend the tradi...