{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T17:06:59Z","timestamp":1785604019863,"version":"3.56.0"},"reference-count":54,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2023,12,11]],"date-time":"2023-12-11T00:00:00Z","timestamp":1702252800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Malaysian Ministry of Higher Education","award":["FRGS\/1\/2021\/ICT06\/UKM\/02\/1"],"award-info":[{"award-number":["FRGS\/1\/2021\/ICT06\/UKM\/02\/1"]}]},{"name":"Malaysian Ministry of Higher Education","award":["FTM1"],"award-info":[{"award-number":["FTM1"]}]},{"name":"Malaysian Ministry of Higher Education","award":["FTM3"],"award-info":[{"award-number":["FTM3"]}]},{"name":"The Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia","award":["FRGS\/1\/2021\/ICT06\/UKM\/02\/1"],"award-info":[{"award-number":["FRGS\/1\/2021\/ICT06\/UKM\/02\/1"]}]},{"name":"The Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia","award":["FTM1"],"award-info":[{"award-number":["FTM1"]}]},{"name":"The Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia","award":["FTM3"],"award-info":[{"award-number":["FTM3"]}]},{"name":"Center for Artificial Intelligence Technology, Universiti Kebangsaan Malaysia","award":["FRGS\/1\/2021\/ICT06\/UKM\/02\/1"],"award-info":[{"award-number":["FRGS\/1\/2021\/ICT06\/UKM\/02\/1"]}]},{"name":"Center for Artificial Intelligence Technology, Universiti Kebangsaan Malaysia","award":["FTM1"],"award-info":[{"award-number":["FTM1"]}]},{"name":"Center for Artificial Intelligence Technology, Universiti Kebangsaan Malaysia","award":["FTM3"],"award-info":[{"award-number":["FTM3"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>In the realm of data analysis and machine learning, achieving an optimal balance of feature importance, known as feature weighting, plays a pivotal role, especially when considering the nuanced interplay between the symmetry of data distribution and the need to assign differential weights to individual features. Also, avoiding the dominance of large-scale traits is essential in data preparation. This step makes choosing an effective normalization approach one of the most challenging aspects of machine learning. In addition to normalization, feature weighting is another strategy to deal with the importance of the different features. One of the strategies to measure the dependency of features is the correlation coefficient. The correlation between features shows the relationship strength between the features. The integration of the normalization method with feature weighting in data transformation for classification has not been extensively studied. The goal is to improve the accuracy of classification methods by striking a balance between the normalization step and assigning greater importance to features with a strong relation to the class feature. To achieve this, we combine Min\u2013Max normalization and weight the features by increasing their values based on their correlation coefficients with the class feature. This paper presents a proposed Correlation Coefficient with Min\u2013Max Weighted (CCMMW) approach. The data being normalized depends on their correlation with the class feature. Logistic regression, support vector machine, k-nearest neighbor, neural network, and naive Bayesian classifiers were used to evaluate the proposed method. Twenty UCI Machine Learning Repository and Kaggle datasets with numerical values were also used in this study. The empirical results showed that the proposed CCMMW significantly improves the classification performance through support vector machine, logistic regression, and neural network classifiers in most datasets.<\/jats:p>","DOI":"10.3390\/sym15122185","type":"journal-article","created":{"date-parts":[[2023,12,11]],"date-time":"2023-12-11T06:56:00Z","timestamp":1702277760000},"page":"2185","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":149,"title":["A Novel Approach for Data Feature Weighting Using Correlation Coefficients and Min\u2013Max Normalization"],"prefix":"10.3390","volume":"15","author":[{"given":"Mohammed","family":"Shantal","sequence":"first","affiliation":[{"name":"Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Selangor, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9142-3612","authenticated-orcid":false,"given":"Zalinda","family":"Othman","sequence":"additional","affiliation":[{"name":"Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Selangor, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Azuraliza Abu","family":"Bakar","sequence":"additional","affiliation":[{"name":"Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Selangor, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"115424","DOI":"10.1016\/j.eswa.2021.115424","article-title":"Feature weighting methods: A review","volume":"184","author":"Manjarres","year":"2021","journal-title":"Expert Syst. 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