Efficient predictive classification model using CACP and GRASP

Hiroyuki Morita, Arthur Mahéo

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

The volume of historical purchasing data has become huge, and it includes many kinds of data attributes. Specifically, categorical data, such as product codes, are difficult to handle. If the product is purchased repeatedly, we can aggregate the data and use the product data as a numerical attribute. However, if the item was purchased only once, we can get only very basic information, such as whether it was purchased or not. To use the information more effectively, we can use a subset of these purchased items as a purchasing pattern within the set of items. Some classification predictive models that use these patterns were proposed, including the classification by aggregating contrast patterns (CACP). However, the model sometimes produces too many specific patterns. This is not a problem for predictions, but interpreting the model can become too complicated to implement efficiently. In this paper, we propose a method to decrease the number of patterns in the classification model for CACP. The proposed method uses the meta-heuristics algorithm known as greedy randomized adaptive search procedure (GRASP). A computational experiment shows that we can remove extra patterns and construct the model, while maintaining its performance level.

Original languageEnglish
Title of host publication2013 3rd World Congress on Information and Communication Technologies, WICT 2013
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages147-153
Number of pages7
ISBN (Electronic)9781479932306
DOIs
Publication statusPublished - 2013
Externally publishedYes
Event2013 3rd World Congress on Information and Communication Technologies, WICT 2013 - Hanoi, Vietnam
Duration: 15 Dec 201318 Dec 2013

Conference

Conference2013 3rd World Congress on Information and Communication Technologies, WICT 2013
CountryVietnam
CityHanoi
Period15/12/1318/12/13

Keywords

  • CACP
  • classification predictive model
  • contrast pattern
  • GRASP

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