DSpace Repository

Applying Decision Trees, Artificial Neural Networks and Support Vector Machine to Classify the Potential of Gas Stations

Show simple item record

dc.contributor.advisor Guha, Sumanta (Chairman) en_US
dc.contributor.author Tanawat Sermvongtrakul en_US
dc.contributor.other Vatcharaporn Esichaikul (Member) en_US
dc.contributor.other Raphael Duboz (Member) en_US
dc.date.accessioned 2015-01-12T10:44:13Z
dc.date.available 2015-01-12T10:44:13Z
dc.date.issued 2012-05 en_US
dc.identifier.other AIT RSPR no.IM-12-07 en_US
dc.identifier.uri http://www.cs.ait.ac.th/xmlui/handle/123456789/496
dc.description.abstract In oil retail industries, gas stations are built to serve households and industries. An operation of a gas station has a very high risk of loss. To reduce that risk, managements need to consider several factors influencing revenues of the station before building a new gas station. With the rapid development in information technology, many different data mining approaches are applied to support management’s decisions. This study focuses on using several data mining techniques to classify the potential of gas stations. The potential means the capability of growth or being without loss; therefore, this study uses sales volume as an indicator of the potential of gas stations. Using classification techniques in the data mining can discover some hidden knowledge on existing gas station data and other related information and the knowledge can also be used for helping the management making a decision for building the new station, which is very beneficial. This study conducted 3 experiments, which use artificial neural networks, support vector machine and decision trees. The results show that using artificial neural networks has the highest accuracy in classifying the potential of gas stations. It is more than 85% accuracy for all models. On the contrary, using support vector machine and decision trees, both of them get lower accuracy rate on testing data. Based on these results, the artificial neural networks technique can serve as a decision support tool for classify a potential of a new gas station, which can reduce the human error in the decision-making process or even help to the management to make a decision with very high accuracy. en_US
dc.description.sponsorship Royal Thai Government en_US
dc.language.iso eng en_US
dc.subject.lcsh Others en_US
dc.title Applying Decision Trees, Artificial Neural Networks and Support Vector Machine to Classify the Potential of Gas Stations en_US
dc.type Research Report en_US


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search DSpace


Advanced Search

Browse

My Account