Rich Feature Combination for Cost-Based Broad Learning System

Abstract

Broad learning system (BLS) is an emerging learning algorithm for the connectionist models, which have enjoyed much popularity on many applications. As an alternative approach of learning in deep structure, the BLS develops an incremental learning neural network that can be modeled in a flexible way, and becomes a promising technique in the field of knowledge discovery and data engineering. To further improve the performance of BLS, our focus is to investigate these algorithms which can enhance the BLS. On one hand, from the viewpoint of feature engineering, unsupervised group-wise encoding is conducted for feature extraction, and broadly fused feature representation is used to improve the ability of BLS, in terms of the learning and reusing multi-level features. On the other hand, for imbalanced learning from disproportionate size of categories instances, a cost-sensitive BLS framework is proposed in this paper, which aims to minimize the total misclassifying cost in classification learning. Finally, we conduct extensive experiments on a wide range of datasets (e.g., computer vision and bug reports) to demonstrate the effectiveness of the proposed BLS framework.

Keywords

• Broad learning system
• broadly-fused feature
• imbalanced learning
• neural network
• cost-sensitive learning