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Data Mining Using an Adaptive HONN Model with Hyperbolic Tangent Neurons

conference contribution
posted on 2023-05-23, 04:57 authored by Shuxiang XuShuxiang Xu
An Artificial Neural Network (ANN) works by creating connections between different processing elements (artificial neurons). ANNs have been extensively used for Data Mining, which extracts hidden patterns and valuable information from large databases. This paper introduces a new adaptive Higher Order Neural Network (HONN) model and applies it in data mining tasks such as determining breast cancer recurrences and predicting incomes base on census data. An adaptive hyperbolic tangent function is used as the neuron activation function for the new adaptive HONN model. 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 adaptive HONN model offers several advantages over conventional ANN models such as better generalisation capabilities as well as abilities in handling missing values in a dataset.

History

Publication title

Knowledge Management and Acquisition for Smart Systems and Services

Editors

Byeong-Ho Kang & Debbie Richards

Pagination

73-81

ISBN

978-3-642-15036-4

Department/School

School of Information and Communication Technology

Publisher

Springer-Verlag

Place of publication

Germany

Event title

PKAW

Event Venue

Daegu, Korea

Date of Event (Start Date)

2010-08-20

Date of Event (End Date)

2010-09-03

Rights statement

© Springer-Verlag Berlin Heidelberg 2010

Repository Status

  • Restricted

Socio-economic Objectives

Information systems, technologies and services not elsewhere classified

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