Adaptive Linear and Normalized Combination of Radial Basis Function Networks for Function Approximation and Regression

Abstract

This paper presents a novel adaptive linear and normalized combination (ALNC) method that can be used to combine the component radial basis function networks (RBFNs) to implement better function approximation and regression tasks. The optimization of the fusion weights is obtained by solving a constrained quadratic programming problem. According to the instantaneous errors generated by the component RBFNs, the ALNC is able to perform the selective ensemble of multiple leaners by adaptively adjusting the fusion weights from one instance to another. The results of the experiments on eight synthetic function approximation and six benchmark regression data sets show that the ALNC method can effectively help the ensemble system achieve a higher accuracy (measured in terms of mean-squared error) and the better fidelity (characterized by normalized correlation coefficient) of approximation, in relation to the popular simple average, weighted average, and the Bagging methods.

A novel machine learning method for cytokine-receptor interaction prediction

“…Moreover, a well-established ensemble classifier is considered more effective than a single classifier. The effectiveness of the classifier combination has been previously reported in numerous biomedical research projects and practical applications [64][65][66].…”

Analysis and Classification of Stride Patterns Associated with Children Development Using Gait Signal Dynamics Parameters and Ensemble Learning Algorithms

“…In the future study, we plan to recruit more gender-matched children participants in the three age groups for more accurate and unbiased statistical analysis of gait patterns during short-term and long-term walking monitoring. More temporal and computational tools [ 28 ] would be considered to analyze other stride phases, such as stance interval, swing interval, and double support time.…”

“…Ensemble learning is also referred to as committee machine learning, which follows a so-called “divide-and-conquer” strategy [ 27 ]. An ensemble paradigm commonly divides a complex classification or regression problem into a few simple tasks with lower computational expense and then combines a group of trained component learners to provide a comprehensive solution [ 28 ]. In the present work, we used the Boosting and Bagging algorithms, two most popular ensemble learning paradigms, to distinguish the gait patterns of the children participants into three age groups.…”

Extended student quality improvement programs of Xiamen University

“…There were 3 student projects supported by the Xiamen University student innovation research grants. Our students have published 7 peer-reviewed international journal papers [14]- [17], [20], [22], [24] and 7 conference papers [25]- [31]. …”