A Self-Tuning Fuzzy Rule-Based Classifier for Data Streams

作者：Shahparast Homeira^*; Hamzeloo Sam; Jahromi Mansoor Zolghadri

来源：International Journal of Uncertainty Fuzziness and Knowledge-Based Systems, 2014, 22(2): 293-303.

DOI：10.1142/S0218488514500147

• Data stream
• concept change
• online rule weighting
• classification

In recent years, tremendous amounts of data streams are generated in different application areas. The new challenges in these data need fast and online data processing, especially in classification problems. One of the most challenging problems in field of data streams that reduces the performance of traditional methods is concept change. To handle this problem, it is necessary to update the classifier system after every alteration of the concept of data. However, updating a classifier can often be a time consuming and expensive process. In this paper, an efficient method is proposed for quickly and easily updating of a fuzzy rule-based classifier by setting a weight for each rule. Then, two online procedures for online adjustment of the rule weights are proposed. The experimental results show the high performance of these methods against a non-weighted approach.

• 出版日期2014-4