Enhancing Classification Algorithms with Metaheuristic Technique
Classification is a process of grouping or placing data into appropriate categories or classes based on specificattributes or features to predict labels or classes of new data based on patternsobserved from previously trained data. Implementing this process uses classification algorithms s...
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INTI International University
2024
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| Online Access: | http://eprints.intimal.edu.my/1954/ http://eprints.intimal.edu.my/1954/1/496 |
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| author | Cokro, Nurwinto Tri Basuki, Kurniawan Misinem, . Tata, Sutabri Yesi Novaria, Kunang |
| author_facet | Cokro, Nurwinto Tri Basuki, Kurniawan Misinem, . Tata, Sutabri Yesi Novaria, Kunang |
| author_sort | Cokro, Nurwinto |
| building | INTI Institutional Repository |
| collection | Online Access |
| description | Classification is a process of grouping or placing data into appropriate categories or classes based on specificattributes or features to predict labels or classes of new data based on patternsobserved from previously trained data. Implementing this process uses classification algorithms such asNaïve Bayes, Support Vector Machine,and Random Forest. However, the classification algorithm cannotclassify data optimally due to the challenges in dealing with variousdata sets. Not all available featureswillmake a solidcontribution to the label of the data class, often in the form of noise or interference. For this reason, it is necessary to carry out a feature selection process. Currently, many feature selection processes have been carried out using correlation values from chi-square and gain-information, but the accuracy of the resultsis often still not good enough. This is because the chi-square and gain-information values are fixed. So,the selection of features is minimaland is not based on the previous learning process or what is known as heuristics. For this reason, in this research,several auxiliary algorithms are introduced to improve the performance of the classification algorithm, namely the meta-heuristic algorithm. Meta-heuristic algorithms are search techniques used to solve complexoptimization problems, and these algorithms can help provide reasonable solutions in a shorter time thanexact methods. In its operation, the metaheuristic algorithm optimizes the feature selection process,which will later be processed using the classification algorithm.Three (3) meta-heuristics were implemented, namely Genetic Algorithm, Particle Swarm Optimization, and Cuckoo Search Algorithm; the experiment was conducted, and the results were collected and analyzed. The result shows that combining Naive Bayes and Genetic Algorithmgives the best performance regarding higher accuracy improvementat +23.77%. |
| first_indexed | 2025-11-14T11:58:11Z |
| format | Article |
| id | intimal-1954 |
| institution | INTI International University |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:58:11Z |
| publishDate | 2024 |
| publisher | INTI International University |
| recordtype | eprints |
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| spelling | intimal-19542024-08-19T01:51:51Z http://eprints.intimal.edu.my/1954/ Enhancing Classification Algorithms with Metaheuristic Technique Cokro, Nurwinto Tri Basuki, Kurniawan Misinem, . Tata, Sutabri Yesi Novaria, Kunang QA Mathematics QA75 Electronic computers. Computer science QA76 Computer software Classification is a process of grouping or placing data into appropriate categories or classes based on specificattributes or features to predict labels or classes of new data based on patternsobserved from previously trained data. Implementing this process uses classification algorithms such asNaïve Bayes, Support Vector Machine,and Random Forest. However, the classification algorithm cannotclassify data optimally due to the challenges in dealing with variousdata sets. Not all available featureswillmake a solidcontribution to the label of the data class, often in the form of noise or interference. For this reason, it is necessary to carry out a feature selection process. Currently, many feature selection processes have been carried out using correlation values from chi-square and gain-information, but the accuracy of the resultsis often still not good enough. This is because the chi-square and gain-information values are fixed. So,the selection of features is minimaland is not based on the previous learning process or what is known as heuristics. For this reason, in this research,several auxiliary algorithms are introduced to improve the performance of the classification algorithm, namely the meta-heuristic algorithm. Meta-heuristic algorithms are search techniques used to solve complexoptimization problems, and these algorithms can help provide reasonable solutions in a shorter time thanexact methods. In its operation, the metaheuristic algorithm optimizes the feature selection process,which will later be processed using the classification algorithm.Three (3) meta-heuristics were implemented, namely Genetic Algorithm, Particle Swarm Optimization, and Cuckoo Search Algorithm; the experiment was conducted, and the results were collected and analyzed. The result shows that combining Naive Bayes and Genetic Algorithmgives the best performance regarding higher accuracy improvementat +23.77%. INTI International University 2024-07 Article PeerReviewed text en cc_by_4 http://eprints.intimal.edu.my/1954/1/496 Cokro, Nurwinto and Tri Basuki, Kurniawan and Misinem, . and Tata, Sutabri and Yesi Novaria, Kunang (2024) Enhancing Classification Algorithms with Metaheuristic Technique. Journal of Data Science, 2024 (22). pp. 1-12. ISSN 2805-5160 http://ipublishing.intimal.edu.my/jods.html |
| spellingShingle | QA Mathematics QA75 Electronic computers. Computer science QA76 Computer software Cokro, Nurwinto Tri Basuki, Kurniawan Misinem, . Tata, Sutabri Yesi Novaria, Kunang Enhancing Classification Algorithms with Metaheuristic Technique |
| title | Enhancing Classification Algorithms with Metaheuristic Technique |
| title_full | Enhancing Classification Algorithms with Metaheuristic Technique |
| title_fullStr | Enhancing Classification Algorithms with Metaheuristic Technique |
| title_full_unstemmed | Enhancing Classification Algorithms with Metaheuristic Technique |
| title_short | Enhancing Classification Algorithms with Metaheuristic Technique |
| title_sort | enhancing classification algorithms with metaheuristic technique |
| topic | QA Mathematics QA75 Electronic computers. Computer science QA76 Computer software |
| url | http://eprints.intimal.edu.my/1954/ http://eprints.intimal.edu.my/1954/ http://eprints.intimal.edu.my/1954/1/496 |