In this paper, a new algorithm for function approximation is proposed to obtain better generalization performance and faster convergent rate. The new algorithm incorporates the architectural constraints from a priori information of the function approximation problem into Extreme Learning Machine. On one hand, according to Taylor theorem, the activation functions of the hidden neurons in this al...

Due to its importance in many applications, the incomplete data mining has received increasing attention in recent years, but there has been little study of the cost-sensitive classification on incomplete data. Therefore this paper proposes the dynamic costsensitive extreme learning machine for classification of incomplete data based on the deep imputation network (DCELMIDC). Firstly, we propos...

Classification systems working on large feature spaces, despite extensive learning, often perform poorly on a group of atypical samples. The problem can be dealt with by incorporating domain knowledge about samples being recognized into the learning process. We present a method that allows to perform this task using a rough approximation framework. We show how human expert’s domain knowledge ex...