Data Mining Using Higher Order Neural Network Models With Adaptive Neuron Activation Functions

Abstract

An information processing algorithm which simulates the way biological neural systems process information, and one of the most popular machine learning algorithms, ANN (Artificial Neural Network) has been extensively used for Data Mining, which extracts hidden patterns and valuable information from large databases. Data Mining is commonly used in a wide range of practices such as accounting, marketing, fraud detection, scientific discovery, etc. This paper introduces a new adaptive Higher Order Neural Network (HONN) model and applies it in data mining tasks such as determining liver disorders and predicting breast cancer recurrences. A new activation function which is a combination of sine and sigmoid functions is used as the neuron activation function for the new HONN model. There are free parameters in the new activation function. The paper compares the new HONN model against a Multi-Layer Perceptron (MLP) with the sigmoid activation function, an RBF Neural Network with the gaussian activation function, and a Recurrent Neural Network (RNN) with the sigmoid activation function. Experimental results show that the new HONN model offers several advantages over conventional ANN models such as improved generalisation capabilities as well as abilities in handling missing values in a dataset.