Integrating boosting and genetic algorithm with supervised classification for socio-demographic based customer targeting | |
| Author | Tutiyaporn Nitichai |
| Call Number | AIT RSPR no.IM-09-10 |
| Subject(s) | Customer services Data mining |
| Note | A research study submitted in partial fulfillment of the requirements for the degree of Master of Science, School of Engineering and Technology |
| Publisher | Asian Institute of Technology |
| Series Statement | Research studies project report ; no. IM-09-10 |
| Abstract | Naïve bayes is a supervised classification technique which aims for classifying or predicting group to unknown data. It assumes class-feature independence and no conditional dependency between classifying features. Generally, this technique delivers result with 60%-90% accuracy. Boosting algorithm is used as an ensemble technique for improve an accuracy of classification algorithms. Genetic algorithm is a promising technique for solution optimization and complex problem. In data mining aspect, genetic algorithm is used in feature subset selection which intends to find a set of predicting features that influence the most to the class label. This paper presents a combination of boosting and genetic algorithm with Naïve bayes in order to increase the prediction accuracy. The experiment is operated to prove that the integration of genetic algorithm, Adaboost with Naïve bayes can improve Naïve bayes’s prediction accuracy. This research runs the experiment byapplyingthe integrated technique over a socio-demographic data set provided by an insurance company. Genetic algorithm is applied for feature selection and Adaboost is utilized for ensemble of Naïve bayes as its based classifier. The company aims to analyze this data set for customer targeting. The result shows that the integrated techniques slightly improve the prediction accuracy. This paper also discusses the reasons causing small improvement. |
| Year | 2009 |
| Corresponding Series Added Entry | Asian Institute of Technology. kkาResearch studies project report ; no. IM-09-10 |
| Type | Research Study Project Report (RSPR) |
| School | School of Engineering and Technology (SET) |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Information Management (IM) |
| Chairperson(s) | Vatcharaporn Esichaikul; |
| Examination Committee(s) | Donyaprueth Krairit;Guha, Sumantha; |
| Scholarship Donor(s) | Royal Thai Government (RTG); |
| Degree | Research Studies Project Report (M.Sc.) - Asian Institute of Technology, 2009 |