Improving rule-based classification using Harmony Search.

ABSTRACT: Classification and associative rule mining are two substantial areas in data mining. Some scientists attempt to integrate these two field called rule-based classifiers. Rule-based classifiers can play a very important role in applications such as fraud detection, medical diagnosis, etc. Numerous previous studies have shown that this type of classifier achieves a higher classification accuracy than traditional classification algorithms. However, they still suffer from a fundamental limitation. Many rule-based classifiers used various greedy techniques to prune the redundant rules that lead to missing some important rules. Another challenge that must be considered is related to the enormous set of mined rules that result in high processing overhead. The result of these approaches is that the

SUBMITTER: Hasanpour H 

PROVIDER: S-EPMC7924428 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature