Improved Learning in Constructive Neural Networks for Pattern Classification using Genetic Algorithm

Objective: To remove rogue entries in the data set chose for training thus making the Neural Network (NN) learning easier. Methods/Analysis: In this research work Genetic Algorithm (GA) is being used on the data sets Wine, Pima Indians Diabetes, Iris, Vehicle and Image Segmentation from the UC Irvine machine learning repository. The refined data sets are then fed to a Constructive Neural Network (CoNN) for pattern classification task. CoNN dispenses an optimal strategy in designing the structures of the multilayer perceptron networks, coupled with supervised algorithms for further learning. They eliminate the requisite of ad-hoc, apriori design of network architecture which is often inappropriate. The M-Tiling algorithm is used for constructing the network and a simple perceptron learning rule for training individual TLUs (Threshold Logic Unit). The CoNNs potential capability is in constructing networks with a network size adequate to the complexity level of the task and in attaining a satisfactory level of efficiency. Findings: Acquiring data from real world problems may result in the frequent occurrence of rogue values. Rogue values result in poor data quality and mislead the learning curve yielding imprecise NN models. A template string prototype is generated with the continual application of GA on the input data. This template string prototype removes the rogue values from the actual training set which results in the reduction of the size of input data set, in turn, makes learning easier. CoNN coupled with GA classifies patterns in Wine, Pima Indians Diabetes, Iris, Vehicle and Image Segmentation datasets available in UC Irvine machine learning repository with faster convergence, more generalization accuracy, and less space. The proposed work is unique since GA is exerted to the training data set before it is fed to the CoNN for learning. The proposed work also demonstrates the improvement of CoNN in terms of convergence, generalization accuracy, and space optimization. In this way, these experimental results add value to the currently existing work. Improvements: The proposed model can be further revamped by using other CoNN learning strategies.