預測客戶潛在的消費行為---使用以遺傳演算法為基礎的組合多層分類器

陳素雯

屏東永達技術學院 財務金融系

email:tamshui@ebtnet.net

摘要

近年來,隨著網際網路的蓬勃發展,全世界的商業公司及政府單位都透過電子商務(e-commerce)來強化本身的競爭力。其中,商業公司則希望透過資料採掘技術(data mining)來爭取顧客及預測客戶潛在的消費行為來獲取利益。但是各式各樣不同的商業公司其經營環境也均各不相同,實在很難找到一種最適當且最有效率的資料採掘演算法來讓各個不同公司同時接受使用。最近出現一個新的趨勢,就是結合多層分類器(multiple classifiers)來改善資料分類結果。在本篇論文中,我們藉由提出一個以遺傳演算法為基礎的組合多層分類器來預測客戶的潛在消費行為。這個方法通過國內一家領導級的電子商務公司測試及評估,我們也針對一般分類問題使用辨識手寫文字來驗証這個方法正確性無誤。這兩個評鑑結果,均顯示我們所提出來的方法確實比單一分類器來得有效率。

關鍵詞: 購買行為分析,多層分類器,電子商務

 

Abstract

 

With the increasing popularity of the Internet, EC companies are eager to learn about their customers using data mining technologies. But the diverse situations of such companies make it difficult to know which is the most effective algorithm for the given problems. Recently, a movement towards combining multiple classifiers has emerged to improve classification results. In this paper, we propose a method for the prediction of the EC customers purchase behavior by combining multiple classifiers based on genetic algorithm. The method was tested and evaluated using Web data from a leading EC company. We also tested the validity of our approach in general classification problems using handwritten numerals. In both cases, our method shows better performance than individual classifiers and other known combing methods we tried.