تطبيق تصنيف بيانات اللوكيميا باستخدام خوارزمية AGA-SVM Applying of Classification Leukemia's Dataset using AGA-SVM Algorithm

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
عمر صابر قاسم ◽  
محمد علي محمد
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Data Classification ◽  
Feature Space ◽  
Support Vector ◽  
Adaptive Genetic Algorithm ◽  
Features Selection ◽  
Svm Algorithm

تعد مسألة اختيار الميزات (Features selection) الضرورية في عملية تصنيف البيانات (Data Classification) من المسائل ذات الأهمية الكبيرة في تحديد كفاءة التقنية المستخدمة للتصنيف خصوصا عندما يكون حجم هذه البيانات كبيرا جدا مثل بيانات اللوكيميا (leukemia) المعتمدة على الجينات. اذ تم استخدام خوارزمية مقترحة(AGA_SVM) مهجنة بين الخوارزمية الجينية المعدلة (Adaptive Genetic Algorithm) مع تقنية الة المتجه الداعم (Support Vector Machine)، اذ تقوم الخوارزمية الجينية المعدلة بتحويل البيانات من فضاء الأنماط العالي البعد (High-D Patterns Space) إلى فضاء الخواص الواطئ (Low-D Feature Space) لأجل تحديد الميزات الضرورية واللازمة لعملية التصنيف والتي تتم من خلال تقنية الة المتجه الداعم. وتبين من خلال التطبيق على بيانات اللوكيميا ان نسبة التصنيف كانت (100%) لحالات التدريب والاختبار بالنسبة للطريقة المقترحة (AGA_SVM) مقارنة مع الطريقة الاعتيادية التي أخطأت في عدة حالات تصنيف، مما يدل على كفاءة الطريقة المقترحة مقارنة مع الطريقة الاعتيادية.

scholarly journals An Adaptive Genetic Algorithm with Recursive Feature Elimination Approach for Predicting Malaria Vector Gene Expression Data Classification using Support Vector Machine Kernels

2021 ◽  
Vol 18 (17) ◽  
Author(s):  
Micheal Olaolu AROWOLO ◽  
Marion Olubunmi ADEBIYI ◽  
Chiebuka Timothy NNODIM ◽  
Sulaiman Olaniyi ABDULSALAM ◽  
Ayodele Ariyo ADEBIYI
Keyword(s):  
Gene Expression ◽  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Malaria Vector ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Adaptive Genetic Algorithm ◽  
Rna Seq ◽  
Gene Expressions

As mosquito parasites breed across many parts of the sub-Saharan Africa part of the world, infected cells embrace an unpredictable and erratic life period. Millions of individual parasites have gene expressions. Ribonucleic acid sequencing (RNA-seq) is a popular transcriptional technique that has improved the detection of major genetic probes. The RNA-seq analysis generally requires computational improvements of machine learning techniques since it computes interpretations of gene expressions. For this study, an adaptive genetic algorithm (A-GA) with recursive feature elimination (RFE) (A-GA-RFE) feature selection algorithms was utilized to detect important information from a high-dimensional gene expression malaria vector RNA-seq dataset. Support Vector Machine (SVM) kernels were used as the classification algorithms to evaluate its predictive performances. The feasibility of this study was confirmed by using an RNA-seq dataset from the mosquito Anopheles gambiae. The technique results in related performance had 98.3 and 96.7 % accuracy rates, respectively. HIGHLIGHTS Dimensionality reduction method based of feature selection Classification using Support vector machine Classification of malaria vector dataset using an adaptive GA-RFE-SVM GRAPHICAL ABSTRACT

A HYBRID OF GENETIC ALGORITHM AND SUPPORT VECTOR MACHINE FOR FEATURES SELECTION AND CLASSIFICATION OF GENE EXPRESSION MICROARRAY

2005 ◽  
Vol 05 (01) ◽  
pp. 91-107 ◽  
Author(s):  
MOHD SABERI MOHAMAD ◽  
SAFAAI DERIS ◽  
ROSLI MD ILLIAS
Keyword(s):  
Gene Expression ◽  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Classification Accuracy ◽  
Hybrid Genetic Algorithm ◽  
Support Vector ◽  
Small Subset ◽  
Features Selection ◽  
Gene Expression Microarray ◽  
Expression Microarray

Constantly improving gene expression technology offer the ability to measure the expression levels of thousand of genes in parallel. Gene expression data is expected to significantly aid in the development of efficient cancer diagnosis and classification platforms. Key issue that needs to be addressed is the selection of small number of genes that contribute to a disease from the thousands of genes measured on microarrays that are inherently noisy. This work deals with finding a small subset of informative genes from gene expression microarray data which maximise the classification accuracy. This paper introduces a new algorithm of hybrid Genetic Algorithm and Support Vector Machine for genes selection and classification task. We show that the classification accuracy of the proposed algorithm is superior to a number of current state-of-the-art methods of two widely used benchmark datasets. The informative genes from the best subset are validated and verified by comparing them with the biological results produced from biologist and computer scientist researches in order to explore the biological plausibility.

scholarly journals Combining the Multi-Genetic Algorithm and Support Vector Machine for Fault Diagnosis of Bearings

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Jianbin Xiong ◽  
Qinghua Zhang ◽  
Qiong Liang ◽  
Hongbin Zhu ◽  
Haiying Li
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Strong Dependence ◽  
Rotating Machinery ◽  
Support Vector ◽  
Evolutionary Search ◽  
Correlation Kernel ◽  
Svm Algorithm ◽  
Kernel Parameters

Overstudy or understudy phenomena can sometimes occur due to the strong dependence of support vector machine (SVM) algorithms on particular parameters and the lack of systems theory relating to parameter selection. In this paper, a parameter optimization algorithm for the SVM is proposed based on multi-genetic algorithm. The algorithm optimizes the correlation kernel parameters of the SVM using evolutionary search principles of multiple swarm genetic algorithms to obtain a superior SVM prediction model. The experimental results demonstrate that by combining the genetic algorithm and SVM algorithm, fault diagnosis can be effectively realized for bearings of rotating machinery.

scholarly journals Heart Rate Variability Classification using Support Vector Machine and Genetic Algorithm

2018 ◽  
Author(s):  
M Ashtiyani ◽  
S Navaei Lavasani ◽  
A Asgharzadeh Alvar ◽  
M R Deevband
Keyword(s):  
Genetic Algorithm ◽  
Heart Rate ◽  
Support Vector Machine ◽  
Heart Rate Variability ◽  
Cardiac Activity ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Ecg Signal ◽  
Features Selection

Background: Electrocardiogram (ECG) is defined as an electrical signal, which represents cardiac activity. Heart rate variability (HRV) as the variation of interval between two consecutive heartbeats represents the balance between the sympathetic and parasympathetic branches of the autonomic nervous system.Objective: In this study, we aimed to evaluate the efficiency of discrete wavelet transform (DWT) based features extracted from HRV which were further selected by genetic algorithm (GA), and were deployed by support vector machine to HRV classification.Materials and Methods: In this paper, 53 ECGs including 3 different beat types (ventricular fibrillation (VF), atrial fibrillation (AF) and also normal sinus rhythm (NSR)), were selected from the MIT/BIH arrhythmia database. The approach contains 4 stages including HRV signal extraction from each ECG signal, feature extraction using DWT (entropy, mean, variance, kurtosis and spectral component β), best features selection by GA and classification of normal and abnormal ECGs using the selected features by support vector machine (SVM).Results: The performance of the classification procedure employing the combination of selected features were evaluated using several measures including accuracy, sensitivity, specificity and precision which resulted in 97.14%, 97.54%, 96.9% and 97.64%, respectively.Conclusion: A comparative analysis with the related existing methods illustrates  the proposed method has a higher potential in the classification of AF and VF. The attempt to classify the ECG signal has been successfully achieved. The proposed method has shown a promising sensitivity of 97.54% which indicates that this technique is an excellent model for computer-aided diagnosis of cardiac arrhythmias.

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