Features of higher order neural network with adaptive neurons

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 has extensive and significant applications in a large variety of areas. 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.