Adaptive Ensemble and Hybrid Models for Classification of Bioinformatics DatasetsTarek Helmy2,3, Mosleh M. Al-Harthi1, Mohamed T. Faheem1,3
- *Corresponding Author:
- Mosleh M. Al-Harthi
College of Engineering, Taif University, Saudi Arabia
E-mail: [email protected]
Received date: August 2011; Revised date: October 2011; Accepted date: November 2011
Clinical databases have accumulated large quantities of information about patients and their clinical histories. Data mining is the search for relationships and patterns within this data that could provide useful knowledge for effective decision-making. Classification analysis is one of the widely adopted data mining techniques for healthcare applications to support and improving the quality of medical diagnosis. This paper presents individual, ensembles and hybrid of computational intelligence techniques such as Support Vector Machine (SVM), Neural Networks (NN), Function Network (FN) and Fuzzy Logic (FL) to classify real bioinformatics datasets. The performance of the proposed computational techniques measured using well known bioinformatics datasets. As expected, the performance of the proposed ensembles and hybrid computational intelligence models is better compared to the monolithic models and overcome the weaknesses of existing classifiers particularly in the classification accuracy.