A multimodal approach for multi-label movie genre classification

Comparison of Machine Learning Techniques for Multi-label Genre Classification

We compare classic text classification techniques with more recent machine learning techniques and introduce a novel architecture that outperforms many state-of-the-art approaches. These techniques are evaluated on a new multi-label classification task, where the task is to predict the genre of a movie based on its subtitle. We show that pre-trained word embeddings contain ’universal’ features ...

A multi-label classification based approach for sentiment classification

A Combination based on OWA Operators for Multi-label Genre Classification of web pages

This paper presents a new method for genre identification that combines homogeneous classifiers using OWA (Ordered Weighted Averaging) operators. Our method uses character n-grams extracted from different information sources such as URL, title, headings and anchors. To deal with the complexity of web pages, we applied MLKNN as a multi-label classifier, in which a web page can be affected by mor...

A Bayesian Nonparametric Approach for Multi-label Classification

Many real-world applications require multi-label classification where multiple target labels are assigned to each instance. In multi-label classification, there exist the intrinsic correlations between the labels and features. These correlations are beneficial for multi-label classification task since they reflect the coexistence of the input and output spaces that can be exploited for predicti...

A Ranking-based KNN Approach for Multi-Label Classification

Multi-label classification has attracted a great deal of attention in recent years. This paper presents an approach exploits a ranking model to learn which neighbor’s labels are more trustable candidates for a weighted KNN-based strategy, and then assigns higher weights to those candidates when making weighted-voting decisions. Our experiment results demonstrate that the proposed method outperf...