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, and etc. Numerous previous studies have shown that this type of classifiers achieves high 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 final selected rules may not be the global best rules. These algorithms are not successful at exploiting search space effectively in order to select the best subset of candidate rules. We merged the Apriori algorithm, harmony search, and classification based association rules (CBA) algorithm for building a rule-based classifier. We applied a modified version of the Apriori algorithm with multiple minimum support for extracting useful rules for each class in the dataset. Instead of using a large number of candidate rules, binary harmony search was utilized for selecting the best subset of rules that appropriate for building a classification model. We applied the proposed method on seventeen benchmark dataset and compared its result with traditional association rule classification algorithms. The statistical results show that our proposed method outperformed other rule-based approaches.

New Associative Classification Method Based on Rule Pruning for Classification of Datasets

“…Therefore, each class label will be associated with a computed score based on the above conditions and the class that has the largest score will be assigned to the test data. Another closely related work by Hasanpour et al [8] that employed binary harmony search method to choose the ideal class association rule showed competitive results in terms of accuracy on 17 data sets. The authors integrated CBA classifier building method with a harmony search method to cut down the number of chosen class association rules.…”

“…In the literature, many algorithms utilize a variety of knowledge-reasoning methodologies, rule pruning, and class predictions for test data. Since AC methods suffer from the exponential growth of the rules [8], this article tackles this problem and proposes a novel pruning method hence this section focuses more on the rule pruning procedures rather than on general AC algorithms in the literature. Rule pruning is the key to success in AC and ensures that any classifiers derived are controllable and usable by the end users.…”

Rule-Based Arabic Sentiment Analysis using Binary Equilibrium Optimization Algorithm

“…DTs and random forests are commonly used as models for generating CRs because of their easy conversion into a set of CRs. C4.5, CART, and ID3 [ 48 , 49 ] are the most well-known DT methods. However, in DTs, the training's simple change may produce a more significant change in the generated model [ 50 ] .…”

Enhancing Cloud Performance Using File Format Classifications

“…Similarly, text classification and text retrieval are used for feature selection in the text files. The metaheuristic algorithms and classifiers such as GA, HS, PSO, NN, and SVM are widely used for text classifications in high dimension space providing high accuracies [32][33][34]. The same algorithms can also be used in the cloud computing environment for load balancing in high dimension text classification datasets [35].…”