TY  - SER
AU  - Tanawat Sermvongtrakul
AU  - Guha,Sumanta
AU  - Vatcharaporn Esichaikul,
AU  - Duboz,Raphael
ED  - Royal Thai Government Fellowship, 
TI  - Applying decision trees, artificial neural networks and support vector machine to classify the potential of gas stations
T2  - Research studies project report
PY  - 2012///
CY  - Pathum Thani, Thailand
PB  - Asian Institute of Technology
KW  - Neural networks (Computer science)
KW  - Decision trees
KW  - Decision support systems
KW  - Data mining
N1  - A research study submitted in partial fulfillment of the requirements forthedegree of Master of Science inInformation Management, School of Engineering of Technology; Research Studies Project Report (M.Sc.) - Asian Institute of Technology, 2012
N2  - In  oil  retail  industries,  gas  stations  are builtto  serve  households  and  industries. An operation  of a  gas  station has  a  very  high  risk  of  loss.  To  reduce  thatrisk,  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{u2019}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 candiscover some hidden knowledge on existing gas station data and other related  information and the  knowledgecan  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  hasthe 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  getloweraccuracy 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.
UR  - http://203.159.5.9/ait-thesis/detail.php?q=B01437
ER  -