Paper Info
Title
Multi-layer heterogeneous ensemble with classifier and feature selection
Abstract
ABSTRACTDeep Neural Networks have achieved many successes when applying to visual, text, and speech information in various domains. The crucial reasons behind these successes are the multi-layer architecture and the in-model feature transformation of deep learning models. These design principles have inspired other sub-fields of machine learning including ensemble learning. In recent years, there are some deep homogenous ensemble models introduced with a large number of classifiers in each layer. These models, thus, require a costly computational classification. Moreover, the existing deep ensemble models use all classifiers including unnecessary ones which can reduce the predictive accuracy of the ensemble. In this study, we propose a multi-layer ensemble learning framework called MUlti-Layer heterogeneous Ensemble System (MULES) to solve the classification problem. The proposed system works with a small number of heterogeneous classifiers to obtain ensemble diversity, therefore being efficiency in resource usage. We also propose an Evolutionary Algorithm-based selection method to select the subset of suitable classifiers and features at each layer to enhance the predictive performance of MULES. The selection method uses NSGA-II algorithm to optimize two objectives concerning classification accuracy and ensemble diversity. Experiments on 33 datasets confirm that MULES is better than a number of well-known benchmark algorithms.
Year
DOI
Venue
2020
10.1145/3377930.3389832
Genetic and Evolutionary Computation Conference
Keywords
DocType
Citations 
Ensemble method, deep learning, multiple classifiers, ensemble of classifiers, feature selection, classifier selection
Conference
1
PageRank 
References 
Authors
0.36
0
6
Name
Order
Citations
PageRank
Tien Thanh Nguyen17912.55
Nang Van Pham210.36
Manh Truong Dang362.14
Anh Vu Luong411.37
John McCall523920.39
A. W.-C. Liew624418.37