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All Journal International Journal of Electrical and Computer Engineering Perfecting a Video Game with Game Metrics
Zahraa Faiz Hussain
AL Salam University College
Computer Science & IT Electrical & Electronics Engineering

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A new model for iris data set classification based on linear support vector machine parameter's optimization Zahraa Faiz Hussain; Hind Raad Ibraheem; Mohammad Alsajri; Ahmed Hussein Ali; Mohd Arfian Ismail; Shahreen Kasim; Tole Sutikno
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 1: February 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (504.778 KB) | DOI: 10.11591/ijece.v10i1.pp1079-1084

Abstract

Data mining is known as the process of detection concerning patterns from essential amounts of data. As a process of knowledge discovery. Classification is a data analysis that extracts a model which describes an important data classes. One of the outstanding classifications methods in data mining is support vector machine classification (SVM). It is capable of envisaging results and mostly effective than other classification methods. The SVM is a one technique of machine learning techniques that is well known technique, learning with supervised and have been applied perfectly to a vary problems of: regression, classification, and clustering in diverse domains such as gene expression, web text mining. In this study, we proposed a newly mode for classifying iris data set using SVM classifier and genetic algorithm to optimize c and gamma parameters of linear SVM, in addition principle components analysis (PCA) algorithm was use for features reduction.
A new model for large dataset dimensionality reduction based on teaching learning-based optimization and logistic regression Hind Raad Ibraheem; Zahraa Faiz Hussain; Sura Mazin Ali; Mohammad Aljanabi; Mostafa Abdulghafoor Mohammed; Tole Sutikno
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 3: June 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i3.13764

Abstract

One of the human diseases with a high rate of mortality each year is breast cancer (BC). Among all the forms of cancer, BC is the commonest cause of death among women globally. Some of the effective ways of data classification are data mining and classification methods. These methods are particularly efficient in the medical field due to the presence of irrelevant and redundant attributes in medical datasets. Such redundant attributes are not needed to obtain an accurate estimation of disease diagnosis. Teaching learning-based optimization (TLBO) is a new metaheuristic that has been successfully applied to several intractable optimization problems in recent years. This paper presents the use of a multi-objective TLBO algorithm for the selection of feature subsets in automatic BC diagnosis. For the classification task in this work, the logistic regression (LR) method was deployed. From the results, the projected method produced better BC dataset classification accuracy (classified into malignant and benign). This result showed that the projected TLBO is an efficient features optimization technique for sustaining data-based decision-making systems.
Co-Authors Ahmed Hussein Ali Hind Raad Ibraheem Mohammad Aljanabi Mohammad Alsajri Mohd Arfian Ismail Mostafa Abdulghafoor Mohammed Shahreen Kasim Sura Mazin Ali Tole Sutikno