Title: Supervised microarray gene retrieval system based on KLFDA and ELM

Authors: Thomas Scaria; T. Christopher

Addresses: Department of Computer Science, St. Pius X College, Kerala, India ' PG and Research Department of Information Technology, Government Arts College, Coimbatore, India

Abstract: Microarray gene data processing has gained considerable research interest these days. However, processing microarray gene data is highly challenging due to its volume. Taking this challenge into account, this work proposes a supervised microarray gene retrieval system which relies on two phases namely, feature dimensionality minimisation and classification. The objective of feature dimensionality minimisation is to make the classification process easier by weeding out the unwanted data. The feature dimensionality of the datasets is minimised by KLFDA and the processed dataset is passed to the classification phase, which is achieved by ELM. The proposed approach is evaluated upon three different benchmark datasets such as colon tumour, central nervous system and ALL-AML. From the experimental results, it is proven that the proposed combination of KLFDA and ELM works better for all the three datasets in terms of accuracy, sensitivity and specificity rates.

Keywords: microarray gene retrieval; classification; feature dimensionality minimisation.

DOI: 10.1504/IJAIP.2024.138567

International Journal of Advanced Intelligence Paradigms, 2024 Vol.27 No.3/4, pp.304 - 317

Received: 23 Apr 2018
Accepted: 14 Jun 2018
Published online: 13 May 2024 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article