Gravitation Theory Based Model for Multi-Label Classification

Boosting-based Multi-label Classification

Multi-label classification is a machine learning task that assumes that a data instance may be assigned with multiple number of class labels at the same time. Modelling of this problem has become an important research topic recently. This paper revokes AdaBoostSeq multi-label classification algorithm and examines it in order to check its robustness properties. It can be stated that AdaBoostSeq ...

Topic Model Based Multi-Label Classification from the Crowd

Multi-label classification is a common supervised machine learning problem where each instance is associated with multiple classes. The key challenge in this problem is learning the correlations between the classes. An additional challenge arises when the labels of the training instances are provided by noisy, heterogeneous crowdworkers with unknown qualities. We first assume labels from a perf...

Multi - label Classification Algorithm Based on Latent Dirichlet Allocation Model

Vector Space Model (VSM) is used frequently in Text Classification (TC). However, it is usually produces a high dimensional feature space which leads to huge cost of computation and storage. Recently, statistic topic model plays an important role in the field of Information Retrieval (IR), TC and Document Clustering. In this chapter, we try to use a kind of statistic model—Latent Dirichlet Allo...

A multi-label classification based approach for sentiment classification

Exploiting Associations between Class Labels in Multi-label Classification

Multi-label classification has many applications in the text categorization, biology and medical diagnosis, in which multiple class labels can be assigned to each training instance simultaneously. As it is often the case that there are relationships between the labels, extracting the existing relationships between the labels and taking advantage of them during the training or prediction phases ...