Comparison of methods for automated feature selection using a Self-Organising Map

Aliyu Usman Ahmad, Andrew Starkey

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution


The effective modelling of high-dimensional data with hundreds to thousands of features remains a challenging task in the field of machine learning. One of the key challenges is the implementation of effective methods for selecting a set of relevant features, which are buried in high-dimensional data along with irrelevant noisy features by choosing a subset of the complete set of input features that predicts the output with higher accuracy comparable to the performance of the complete input set. Kohonen’s Self Organising Neural Network MAP has been utilized in various ways for this task. In this work, a review of the appropriate application of multiple methods for this task is carried out. The feature selection approach based on analysis of the Self Organising network result after training is presented with comparison of performance of two methods.
Original languageEnglish
Title of host publicationEngineering Applications of Neural Networks
Subtitle of host publication17th International Conference, EANN 2016, Aberdeen, UK, September 2-5, 2016, Proceedings
EditorsChrisina Jayne, Lazaros Iliadis
Number of pages13
VolumeCCIS 269
ISBN (Electronic)978-3-319-44188-7
ISBN (Print)978-3-319-44187-0
Publication statusPublished - 2016

Publication series

NameCommunications in Computer and Information Science
ISSN (Print)1865-0929


  • Clustering
  • Self-organising neural network MAP
  • Feature selection
  • Engineering optimisation


Dive into the research topics of 'Comparison of methods for automated feature selection using a Self-Organising Map'. Together they form a unique fingerprint.

Cite this