Pattern Recognition for Brain-Computer Interfaces by Combining Support Vector Machine with Adaptive Genetic Algorithm

2007 ◽  
pp. 307-316 ◽  
Author(s):  
Banghua Yang ◽  
Shiwei Ma ◽  
Zhihua Li
Keyword(s):  
Genetic Algorithm ◽  
Pattern Recognition ◽  
Support Vector Machine ◽  
Support Vector ◽  
Adaptive Genetic Algorithm ◽  
Brain Computer Interfaces ◽  
Computer Interfaces

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

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.

DMiner-I: A software tool of data mining and its applications

Robotica ◽  
2002 ◽  
Vol 20 (5) ◽  
pp. 499-508
Author(s):  
Jie Yang ◽  
Chenzhou Ye ◽  
Nianyi Chen
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Genetic Algorithm ◽  
Pattern Recognition ◽  
Support Vector Machine ◽  
Knowledge Representation ◽  
Decision Trees ◽  
Software Tool ◽  
Support Vector ◽  
Function Models

SummaryA software tool for data mining (DMiner-I) is introduced, which integrates pattern recognition (PCA, Fisher, clustering, HyperEnvelop, regression), artificial intelligence (knowledge representation, decision trees), statistical learning (rough set, support vector machine), and computational intelligence (neural network, genetic algorithm, fuzzy systems). It consists of nine function models: pattern recognition, decision trees, association rule, fuzzy rule, neural network, genetic algorithm, HyperEnvelop, support vector machine and visualization. The principle, algorithms and knowledge representation of some function models of data mining are described. Nonmonotony in data mining is dealt with by concept hierarchy and layered mining. The software tool of data mining is realized byVisual C++under Windows 2000. The software tool of data mining has been satisfactorily applied in the prediction of regularities of the formation of ternary intermetallic compounds in alloy systems, and diagnosis of brain glioma.

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