Support vector Self-Organizing learning for imbalanced medical data

Yok Yen Nguwi, Siu Yeung Cho

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

8 Citations (Scopus)

Abstract

The aim of computational learning algorithm is to establish grounds that works for any types of data, once and for all. However, majority of the classifiers assume the datasets are balanced. This research is targeted towards obtaining a model that is able to handle imbalanced data well. This work progresses by examining the efficiency of the model in evaluating imbalanced medical data. The model adopted a derivation of support vector machines in selecting variables. The classification phase uses unsupervised learning algorithm of Emergent Self-Organizing Map. Experimental results show that the criterion based on weight vector derivative achieves good results and performs consistently well over imbalance data.

Original languageEnglish
Title of host publication2009 International Joint Conference on Neural Networks, IJCNN 2009
Pages2250-2255
Number of pages6
DOIs
Publication statusPublished - 2009
Externally publishedYes
Event2009 International Joint Conference on Neural Networks, IJCNN 2009 - Atlanta, GA, United States
Duration: 14 Jun 200919 Jun 2009

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Conference

Conference2009 International Joint Conference on Neural Networks, IJCNN 2009
Country/TerritoryUnited States
CityAtlanta, GA
Period14/06/0919/06/09

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Fingerprint

Dive into the research topics of 'Support vector Self-Organizing learning for imbalanced medical data'. Together they form a unique fingerprint.

Cite this