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Features of higher order neural network with adaptive neurons

conference contribution
posted on 2023-05-23, 04:55 authored by Shuxiang XuShuxiang Xu
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.

History

Publication title

Proceeding of the 2nd International Conference on Software Engineering and Data Mining (SEDM2010)

Editors

G Kou, Y Peng, F Ko, YW Chen, T Tateyama

Pagination

484-488

ISBN

978-1-4244-7324-3

Department/School

School of Information and Communication Technology

Publisher

IEEE Computer Society

Place of publication

Los Alamitos, CA

Event title

Software Engineering and Data Mining (SEDM) International Conference

Event Venue

Chengdu, China

Date of Event (Start Date)

2010-06-23

Date of Event (End Date)

2010-06-25

Rights statement

Copyright © 2010 IEEE

Repository Status

  • Restricted

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