k-NN-based feature weights adjustment algorithm for case similarity measurement

Computer Engineering and Applications ›› 2007, Vol. 43 ›› Issue (23): 8-11.

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k-NN-based feature weights adjustment algorithm for case similarity measurement

YANG Jian¹，YANG Xiao-guang²，LIU Xiao-bin³，QIN Fan¹

1.Business School，Nankai University，Tianjin 300071，China
2.Computer Science Department，Langfang Normal University，Langfang，Hebei 065000，China
3.Fundament Department，The Armed Police Academy，Langfang，Hebei 065000，China

Received:1900-01-01 Revised:1900-01-01 Online:2007-08-11 Published:2007-08-11
Contact: YANG Jian

一种基于k-NN的案例相似度权重调整算法

杨健¹，杨晓光²，刘晓彬³，秦凡¹

1.南开大学商学院，天津 300071
2.廊坊师范学院计算机科学与技术系，河北廊坊 065000
3.中国人民武装警察部队学院基础部，河北廊坊 065000

通讯作者: 杨健

Abstract

Abstract: Following the classical approaches of case similarity calculation in CBR retrieval，this paper improves the traditional algorithm of k-NN.After feature reduction，based on the hypothesis that time factor has a significant affect on the adoptability of the history cases，a small scale algorithm for case feature weight calculation called TSBMPSA is proposed.The algorithm is suitable for numeric features.

Key words: Case-Based Reasoning（CBR）, case similarity, case retrieval, k-NN algorithm, feature weight

摘要： 对于CBR中的案例检索问题，结合经典案例相似度计算方法，对目前在各实际系统中应用最为广泛的k-NN算法进行改进。经过特征约简，在假设时间因素对历史案例可采纳程度有显著影响基础上，提出了一种小规模的基于时序的案例特征权重多阶段调整算法。该算法适用于数值型特征项相似度计算。

关键词: 基于案例推理, 案例相似度, 案例检索, k-NN算法, 特征权重

YANG Jian¹，YANG Xiao-guang²，LIU Xiao-bin³，QIN Fan¹. k-NN-based feature weights adjustment algorithm for case similarity measurement[J]. Computer Engineering and Applications, 2007, 43(23): 8-11.

杨健¹，杨晓光²，刘晓彬³，秦凡¹. 一种基于k-NN的案例相似度权重调整算法[J]. 计算机工程与应用, 2007, 43(23): 8-11.

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k-NN-based feature weights adjustment algorithm for case similarity measurement

一种基于k-NN的案例相似度权重调整算法

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