Identifying the Machine Learning Techniques for Classification of Target Datasets

Given the dynamic and convoluted nature of numerous datasets, the necessity of enhancing performance outcomes and handling multiple datasets has become more challenging. To handle these issues effectively and improve the quality of multiple approaches, the capabilities of various Machine Learning t...

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Autores principales:	Abdul Ahad Abro, Mohammed Abebe Yimer, Zeeshan Bhatti
Formato:	article
Lenguaje:	EN
Publicado:	Sukkur IBA University 2020
Materias:	Machine Learning Data Mining K-Nearest Neighbor Logistic Regression Naive Bayes Computer engineering. Computer hardware TK7885-7895 Mathematics QA1-939 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/428b00e32bf64dbaa9f27a2c36e72134
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spelling	oai:doaj.org-article:428b00e32bf64dbaa9f27a2c36e721342021-11-11T10:07:31ZIdentifying the Machine Learning Techniques for Classification of Target Datasets10.30537/sjcms.v4i1.5802520-07552522-3003https://doaj.org/article/428b00e32bf64dbaa9f27a2c36e721342020-07-01T00:00:00Zhttp://localhost:8089/sibajournals/index.php/sjcms/article/view/580https://doaj.org/toc/2520-0755https://doaj.org/toc/2522-3003 Given the dynamic and convoluted nature of numerous datasets, the necessity of enhancing performance outcomes and handling multiple datasets has become more challenging. To handle these issues effectively and improve the quality of multiple approaches, the capabilities of various Machine Learning techniques such as K-Nearest Neighbor (KNN), Logistic Regression (LR), Naive Bayes(NB) and Support Vector Machine (SVM) have been utilized in this study. In this paper, the binary classification method using five different datasets and many predictor variables have been utilized. Moreover, this research has mainly focused on determining the classification of data into the subsets that share the standard designs. In this regard, many approaches had been studied extensively and used to achieve better yields from the existing literature; however, they were inadequate to provide efficient outcomes. By applying four Supervised ML classification algorithms along with the UCI Datasets of ML Repository, the robustness of the method is progressed. The proposed mechanism is assessed by adopting five performance criteria concerning the accuracy, AUC (Area Under Curve), precision, recall and F-measure values. The current study experimental results revealed that there is a significant improvement in the confusion matrix rate compared with a similar study and this method can also be used for machine learning problems such as binary classification. Abdul Ahad AbroMohammed Abebe YimerZeeshan BhattiSukkur IBA UniversityarticleMachine LearningData MiningK-Nearest NeighborLogistic RegressionNaive BayesComputer engineering. Computer hardwareTK7885-7895MathematicsQA1-939Electronic computers. Computer scienceQA75.5-76.95ENSukkur IBA Journal of Computing and Mathematical Sciences, Vol 4, Iss 1 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Machine Learning Data Mining K-Nearest Neighbor Logistic Regression Naive Bayes Computer engineering. Computer hardware TK7885-7895 Mathematics QA1-939 Electronic computers. Computer science QA75.5-76.95
spellingShingle	Machine Learning Data Mining K-Nearest Neighbor Logistic Regression Naive Bayes Computer engineering. Computer hardware TK7885-7895 Mathematics QA1-939 Electronic computers. Computer science QA75.5-76.95 Abdul Ahad Abro Mohammed Abebe Yimer Zeeshan Bhatti Identifying the Machine Learning Techniques for Classification of Target Datasets
description	Given the dynamic and convoluted nature of numerous datasets, the necessity of enhancing performance outcomes and handling multiple datasets has become more challenging. To handle these issues effectively and improve the quality of multiple approaches, the capabilities of various Machine Learning techniques such as K-Nearest Neighbor (KNN), Logistic Regression (LR), Naive Bayes(NB) and Support Vector Machine (SVM) have been utilized in this study. In this paper, the binary classification method using five different datasets and many predictor variables have been utilized. Moreover, this research has mainly focused on determining the classification of data into the subsets that share the standard designs. In this regard, many approaches had been studied extensively and used to achieve better yields from the existing literature; however, they were inadequate to provide efficient outcomes. By applying four Supervised ML classification algorithms along with the UCI Datasets of ML Repository, the robustness of the method is progressed. The proposed mechanism is assessed by adopting five performance criteria concerning the accuracy, AUC (Area Under Curve), precision, recall and F-measure values. The current study experimental results revealed that there is a significant improvement in the confusion matrix rate compared with a similar study and this method can also be used for machine learning problems such as binary classification.
format	article
author	Abdul Ahad Abro Mohammed Abebe Yimer Zeeshan Bhatti
author_facet	Abdul Ahad Abro Mohammed Abebe Yimer Zeeshan Bhatti
author_sort	Abdul Ahad Abro
title	Identifying the Machine Learning Techniques for Classification of Target Datasets
title_short	Identifying the Machine Learning Techniques for Classification of Target Datasets
title_full	Identifying the Machine Learning Techniques for Classification of Target Datasets
title_fullStr	Identifying the Machine Learning Techniques for Classification of Target Datasets
title_full_unstemmed	Identifying the Machine Learning Techniques for Classification of Target Datasets
title_sort	identifying the machine learning techniques for classification of target datasets
publisher	Sukkur IBA University
publishDate	2020
url	https://doaj.org/article/428b00e32bf64dbaa9f27a2c36e72134
work_keys_str_mv	AT abdulahadabro identifyingthemachinelearningtechniquesforclassificationoftargetdatasets AT mohammedabebeyimer identifyingthemachinelearningtechniquesforclassificationoftargetdatasets AT zeeshanbhatti identifyingthemachinelearningtechniquesforclassificationoftargetdatasets
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Identifying the Machine Learning Techniques for Classification of Target Datasets

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