Universiti Teknologi Malaysia Institutional Repository

Artificial neural network — Naïve bayes fusion for solving classification problem of imbalanced dataset

Adam, A. and Shapiai, Mohd. Ibrahim and Ibrahim, Zuwairie and Khalid, Marzuki (2011) Artificial neural network — Naïve bayes fusion for solving classification problem of imbalanced dataset. In: 2011 4th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO). IEEE, Danvers, Massachusetts, 1-5 . ISBN 978-1-4577-0003-3

Full text not available from this repository.

Official URL: http://dx.doi.org/10.1109/ICMSAO.2011.5775584


Incorporating knowledge from domain expert to a classifier is one of the techniques which require to be considered in solving imbalanced dataset problems. In this study, the proposed technique is a development to extend the process for imbalanced dataset where the individual classification system has already been designed for balanced data set. This paper introduces a methodology and preliminary results which are used to investigate whether the proposed approach is possible to improve a classifier's performance when domain expert is employed to the nai¨ve bayes classifier. Domain expert is an additional knowledge which is produced by expert system (neural network) and then become an additional input to the nai¨ve bayes classifier. By using several benchmark data sets from the UCI Machine Learning Repository, the results of the proposed technique show an improvement as compared to the conventional nai¨ve bayes classifier.

Item Type:Book Section
Uncontrolled Keywords:domain expert, expert knowledge, imbalanced data set, knowledge engineering, naïve bayes classifier, neural network classifier
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:Electrical Engineering
ID Code:29593
Deposited By: Liza Porijo
Deposited On:18 Mar 2013 13:43
Last Modified:04 Feb 2017 07:00

Repository Staff Only: item control page