Classification using Redundant Mapping in Modular Neural Networks

Abstract Classification is a major problem of study that involves formulation of decision boundaries based o

Diagnosis of Breast Cancer by Modular Evolutionary Neural Networks

Abstract Machine learning and pattern recognition play a vital role in the field of biomedical engineering, where the task is to identify or classify a disease based on a set of observations. The inability of a single method to effectively solve the problem gives rise to the use multiple models for solving the same problem in a 'Mixture of Experts' mode. Further the data may be too large for any system to effectively solve the problem. This motivates the use of computational modularity in the system where a number of modules independently solve part of the problem. In this paper we construct a Mixture of Experts model where a number of different techniques are applied to solve the same problem. The individual decision by each of these experts is fused by an integrator that gives the final output. Each of the units is a complex modular neural network. The first modularity clusters the entire input space into a set of modules. The second modularity divides the number of attributes. Each cluster is a neural network that solves the problem. The individual neural networks are evolved using Genetic Algorithms which optimizes both the architecture and the parameters. The complete system is used for the diagnosis of Breast Cancer. Experimental results show that the proposed system outperforms the traditional simple and hybrid approaches. The system on the whole is highly scalable to both number of attributes and data items