A learning algorithm for feature weighting in case-based reasoning
by Maryam Ghadami; Seyed-Mohammad Seyedhosseini; Ahmad Makui
International Journal of Knowledge Engineering and Data Mining (IJKEDM), Vol. 2, No. 2/3, 2012

Abstract: Case-based reasoning (CBR) is a methodology for problem-solving and decision making in complex business environment. This methodology is based on the reasoning of earlier experiences to solve new problems. Among well-known methods for case retrieval, K-nearest neighbour (K-NN) is more widely used. But since it is highly sensitive to input features, irrelevant features may cause an increase in the prediction error. In this research, a learning algorithm is developed by applying performance feedback to assign weight setting. The main idea behind the algorithm is to recognise cases which are incorrectly selected as nearest neighbours due to ineffective feature weighting assignment, and attempts to iteratively modify feature weights. This approach is applied to provide suggestions for better decision making and managing strategies of products in the middle of life phase. The dataset of a department store, including various products, have been used. K-fold approach is used to evaluate the prediction accuracy rate. Empirical results are compared with artificial neural network, classical CBR, CBR-GA algorithms. The results indicate that the predictive performance of the model, compare with two CBR-based algorithms, in specific case is more effective.

Online publication date: Sat, 13-Sep-2014

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Knowledge Engineering and Data Mining (IJKEDM):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com