NDLI: A Novel Multi-Objective Genetic Algorithm Approach to Artificial Neural Network Topology Optimisation: The Breast Cancer Classification Problem

Content Provider	IEEE Xplore Digital Library
Author	Bevilacqua, V. Mastronardi, G. Menolascina, F. Pannarale, P. Pedone, A.
Copyright Year	2006
Description	Author affiliation: DEE -Politecnico di Bari, Bari (Bevilacqua, V.; Mastronardi, G.; Menolascina, F.; Pannarale, P.; Pedone, A.)
Abstract	This paper presents a novel approach to artificial neural network (ANN) topology optimisation that uses multi-objective genetic algorithm in order to find the best network configuration for the Wisconsin breast cancer database (WBCD) classification problem. The WBCD [Mangasarian, OL., et al., 1995][Mangasarian, OL., et al.][Wolberg, WH., et al., 1995] is a publicly available database composed by 699 cases, each of which is defined by 11 parameters. The former first 10 values of each record account for geometrical features of cells extracted with FNA biopsy. The last parameter represents the nature of the tumour; two classes of tumour are considered in this database: benignant and malignant tumours. An Intelligent System, IDEST, was designed and implemented. At the core of this system there's an Artificial Neural Network that is able to classify cases. The design of such an ANN is a non trivial task and choices incoherent with the problem could lead to instability of the network. For these reasons a fixed topology genetic algorithm (GA) approach was used to find an optimal topology for the given problem. In a second step a multi-objective GA (MOGA) was developed and employed in order to refine the search in the "topology space". Results shown by the IDEST demonstrate the great potentialities of similar approaches.
Starting Page	1958
Ending Page	1965
File Size	2998435
Page Count	8
File Format	PDF
ISBN	0780394909
DOI	10.1109/IJCNN.2006.246940
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-07-16
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Genetic algorithms Artificial neural networks Network topology Breast cancer Spatial databases Tumors Feature extraction Biopsy Intelligent systems Artificial intelligence
Content Type	Text
Resource Type	Article

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