Abstract
This paper proposes a new approach to address the optimal design of a Feed-forward Neural Network (FNN) based classifier. The originality of the proposed methodology, called CMOA, lie in the use of a new constraint handling technique based on a self-adaptive penalty procedure in order to direct the entire search effort towards finding only Pareto optimal solutions that are acceptable. Neurons and connections of the FNN Classifier are dynamically built during the learning process. The approach includes differential evolution to create new individuals and then keeps only the non-dominated ones as the basis for the next generation. The designed FNN Classifier is applied to six binary classification benchmark problems, obtained from the UCI repository, and results indicated the advantages of the proposed approach over other existing multi-objective evolutionary neural networks classifiers reported recently in the literature.
Highlights
The problem of classification is perhaps one of the most widely studied in the data mining and machine learning communities
This does not imply that a forward Neural Network (FNN) can learn the underlying functional mapping between the input data and the desired output
This paper proposes a new Constrained Multi-Objective Optimization Algorithm (CMOA) to achieve a solution to the learning of a FNN based classifier, characterized by good generalization properties
Summary
The problem of classification is perhaps one of the most widely studied in the data mining and machine learning communities. Pareto-based multi-objective algorithms [6] have been adopted for the design of FNN and they have attracted much interest, with promising results Such approaches are usually implemented by means of error minimization while controlling the complexity of the model [6]. The primary goal in CMOA is to find or to approximate the set of Pareto optimal solutions [8] that results into trade-offs among learning performance (expressed in terms of sensitivity and specificity) and complexity. CMOA aim at splitting objective space into two areas separating the acceptable solutions from unacceptable ones and uses a specific constraint handling technique based on a self-adaptive penalty procedure in order to direct the entire search effort towards finding only Pareto optimal solutions that are acceptable
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