Online feature selection for multi-label classification in multi-objective optimization framework

• Unlike common multi-label feature selection, the proposed method derives from different cognitive standpoint.

In multi-label data, each instance is associated with a set of labels, instead of one label. Similar to single-label data, feature selection plays an important role in improving classification performance. In multi-label classification,...

• A multi-objective online multi-label feature selection method is proposed.
• A novel metric based on mutual information was proposed to evaluate features.
• A novel algorithm was designed to update a fixed-sized feature pool.
• ...

Multi-label classification methods aim at assigning more than one label to each instance. In many real-world classification problems such as image multi-label classification tasks such as cancer detection, and text classification, we faced with ...

Feature selection is an important data preprocessing method before classification. Multi-objective optimization algorithms have been proved an effective way to solve feature selection problems. However, there are few studies on multi-objective ...