{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T16:11:29Z","timestamp":1776874289995,"version":"3.51.2"},"reference-count":79,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2021,2,23]],"date-time":"2021-02-23T00:00:00Z","timestamp":1614038400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>The accurate classification of microbes is critical in today\u2019s context for monitoring the ecological balance of a habitat. Hence, in this research work, a novel method to automate the process of identifying microorganisms has been implemented. To extract the bodies of microorganisms accurately, a generalized segmentation mechanism which consists of a combination of convolution filter (Kirsch) and a variance-based pixel clustering algorithm (Otsu) is proposed. With exhaustive corroboration, a set of twenty-five features were identified to map the characteristics and morphology for all kinds of microbes. Multiple techniques for feature selection were tested and it was found that mutual information (MI)-based models gave the best performance. Exhaustive hyperparameter tuning of multilayer layer perceptron (MLP), k-nearest neighbors (KNN), quadratic discriminant analysis (QDA), logistic regression (LR), and support vector machine (SVM) was done. It was found that SVM radial required further improvisation to attain a maximum possible level of accuracy. Comparative analysis between SVM and improvised SVM (ISVM) through a 10-fold cross validation method ultimately showed that ISVM resulted in a 2% higher performance in terms of accuracy (98.2%), precision (98.2%), recall (98.1%), and F1 score (98.1%).<\/jats:p>","DOI":"10.3390\/e23020257","type":"journal-article","created":{"date-parts":[[2021,2,23]],"date-time":"2021-02-23T12:40:16Z","timestamp":1614084016000},"page":"257","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["An Improvised Machine Learning Model Based on Mutual Information Feature Selection Approach for Microbes Classification"],"prefix":"10.3390","volume":"23","author":[{"given":"Anaahat","family":"Dhindsa","sequence":"first","affiliation":[{"name":"Department of Electronics and Communication Engineering, Chandigarh University, Gharuan, Punjab 140413, India"},{"name":"University Institute of Engineering and Technology, Panjab University, Chandigarh 160014, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sanjay","family":"Bhatia","sequence":"additional","affiliation":[{"name":"Post Graduate Department of Zoology, University of Jammu, Kashmir 180006, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sunil","family":"Agrawal","sequence":"additional","affiliation":[{"name":"University Institute of Engineering and Technology, Panjab University, Chandigarh 160014, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Balwinder Singh","family":"Sohi","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering, Chandigarh University, Gharuan, Punjab 140413, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1016\/j.biocon.2016.09.005","article-title":"Essential Biodiversity Variables for Measuring Change in Global Freshwater Biodiversity","volume":"3","author":"Turak","year":"2017","journal-title":"Biol. 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