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Gene selection based on multi-class support vector machines and genetic algorithms

Bruno Feres de Souza, André Ponce de Leon F de Carvalho
2005 Genetics and Molecular Research  
We present a novel approach to the gene selection problem in multi-class gene expression-based cancer classification, which combines support vector machines and genetic algorithms.  ...  This new method is able to select small subsets and still improve the classification accuracy.  ...  CONCLUSIONS We have presented a novel gene selection method for multi-class problems based on GAs and SVM.  ... 
pmid:16342045 fatcat:3iz46v2itnddzfhyq7rnfugy44

A Hybrid Approach For Selection Of Relevant Features For Microarray Datasets

R. K. Agrawal, Rajni Bala
2007 Zenodo  
A new fitness function for genetic algorithm is defined that focuses on selecting the smallest set of genes that provides maximum accuracy. Experiments have been carried on four well-known datasets1.  ...  In this paper, we have proposed a hybrid method using feature ranking and wrapper method (Genetic Algorithm with multiclass SVM) to identify a set of relevant genes that classify the data more accurately  ...  So this set of selected genes is further reduced with the help of genetic algorithm combined with multi-class SVM.  ... 
doi:10.5281/zenodo.1071300 fatcat:5ifwkkbuefhn5mu34xwrkxpe44

Hybrid feature selection methods for the Classification of Cancer in Micro-array Gene expression data: a Survey

2020 International Journal of Advanced Trends in Computer Science and Engineering  
The Micro-array data analysis is the method to remove redundant and obsolete genes, to identify the most significant genes.  ...  A collection of features (genes) is one of the most successful approaches to face these challenges.  ...  The research was performed on 11 discrete cancer and multi-class datasets.  ... 
doi:10.30534/ijatcse/2020/275952020 fatcat:gtag5sze3vdftaw5s4ub4olnyq

Gene expression data classification using genetic algorithm based feature selection

2021 Turkish Journal of Electrical Engineering and Computer Sciences  
In this study, hybrid methods are proposed for feature selection and classification of gene expression datasets. 3 In the proposed genetic algorithm / support vector machine (GA-SVM) and genetic algorithm  ...  / k nearest neighbor (GA-4 KNN) hybrid methods, genetic algorithm is improved by Pearson correlation coefficient, Relief-F or mutual information. 5 Crossover and selection operations of the genetic algorithm  ...  Gene Selection and sample classification on microarray data based on adaptive genetic algorithm/k-nearest neighbor method.  ... 
doi:10.3906/elk-2102-110 fatcat:vgvbbowi75cnja6srywomzcud4

A Generic Classifier-Ensemble Approach for Biomedical Named Entity Recognition [chapter]

Zhihua Liao, Zili Zhang
2012 Lecture Notes in Computer Science  
The proposed approach is tested on the benchmark dataset -GENIA version 3.02 corpus, and compared with both individual best SVM classifier and SVM-classifier ensemble algorithm as well as other machine  ...  A multi-objective Genetic algorithm (GA) is employed as the classifier selector to facilitate the ensemble classifier to improve the overall sample classification accuracy.  ...  In this paper, we propose a generic genetic classifier-ensemble approach, which employs multi-objective genetic algorithm and SVM based classifiers to construct an ensemble classifier.  ... 
doi:10.1007/978-3-642-30217-6_8 fatcat:3haex6oxnzf2na37drlf5u36py

The Performance of Bio-Inspired Evolutionary Gene Selection Methods for Cancer Classification Using Microarray Dataset

Hala M. Alshamlan, Ghada H. Badr, Yousef A. Alohali
2014 International Journal of Bioscience Biochemistry and Bioinformatics  
Microarray based gene expression profiling has become an important and promising dataset for cancer classification that are used for diagnosis and prognosis purposes.  ...  And, we prove that the Bio-Inspired evolutionary gene selection methods have superior classification accuracy with minimum number of selected genes.  ...  For this motivation, bio inspired evolutionary algorithms may represent a helpful and effective tool in the binary and multi class cancer classification based on microarray gene expression data.  ... 
doi:10.7763/ijbbb.2014.v4.332 fatcat:msd7sqjcsrc4rozf5mdsng6tyu

A Survey on Hybrid Feature Selection Methods in Microarray Gene Expression Data For Cancer Classification

Nada Almugren, Hala Alshamlan
2019 IEEE Access  
One of the most effective methods to overcome these challenges is feature (gene) selection.  ...  The Microarray data analysis is the process of finding the most informative genes as well as remove redundant and irrelevant genes.  ...  ALGORITHM Li and Yin [60] proposed a Multi-Objective Binary Biogeography (MOBBBO) based gene selection method.  ... 
doi:10.1109/access.2019.2922987 fatcat:hnfhi5lrnbaozplhjidlkta6su

B-cell and T-cell Leukemia Classification using Genetic Algorithm, PCA, SVM and ANN

Sakshi Sharma, Ajay Kumar
2019 International Journal of Computer Applications  
In the first phase, a hybrid approach of principle component analysis is and genetic algorithm is applied on leukemia microarray dataset for extracting relevant features.  ...  Microarray technology can be used for learning number of genes expressions at one time.  ...  : Flow chart of PCA algorithm 4.2 Genetic algorithm Fig. 2 Feature Microarr ay leukemia gene Extraction/ Selection by using PCA and Genetic algorithm Cancer gene classificati on by FFBNN and SVN expressio  ... 
doi:10.5120/ijca2019919380 fatcat:tafhktyhnfdv7mbs6tbdb77nwe

ABC-SVM: Artificial Bee Colony and SVM Method for Microarray Gene Selection and Multi Class Cancer Classification

Hala M. Alshamlan, Ghada H. Badr, Yousef A. Alohali
2016 International Journal of Machine Learning and Computing  
We evaluate the performance of the proposed ABC-SVM algorithm by conducting extensive experiments on six binary and multi-class microarrays dataset.  ...  The experimental results prove that ABC-SVM algorithm is promising approach for solving gene selection and cancer classification problems, and achieves the highest classification accuracy together with  ...  The proposed algorithm is tested using six binary and multi-class gene expression microarray datasets and is also compared with genetic algorithm when combined with SVM (GA-SVM), and particle swarm optimization  ... 
doi:10.18178/ijmlc.2016.6.3.596 fatcat:3b3xd7uerfd65doprkjb434e6i

Gene selection from microarray expression data: A Multi-objective PSO with adaptive K-nearest neighborhood [article]

Yasamin Kowsari, Sanaz Nakhodchi, Davoud Gholamiangonabadi
2022 arXiv   pre-print
Then, after normalization, it is used Multi-Objective Particle Swarm Optimization (MOPSO) for feature selection and employed Adaptive K-Nearest Neighborhood (KNN) for cancer disease classification.  ...  Cancer detection is one of the key research topics in the medical field.  ...  A hybrid technique for gene selection, called ensemble multi-population adaptive genetic algorithm (EMPAGA) proposed In [16] , can overlook the irrelevant genes and classify cancer accurately.  ... 
arXiv:2205.15020v1 fatcat:xocakuwcyvcbxj2bwc4shwegdi

A Transformer Fault Diagnosis Model Using an Optimal Hybrid Dissolved Gas Analysis Features Subset with Improved Social Group Optimization-Support Vector Machine Classifier

Jiake Fang, Hanbo Zheng, Jiefeng Liu, Junhui Zhao, Yiyi Zhang, Ke Wang
2018 Energies  
), GASVM (SVM classifier optimized by genetic algorithm optimization), PSOSVM (SVM classifier optimized by particle swarm optimization), and SVM diagnostic models.  ...  Then, to eliminate the interference of weak-relevant and irrelevant features, the genetic-algorithm-SVM-feature-screen (GA-SVM-FS) model was built to screen out the optimal hybrid DGA features subset (  ...  Figure 2 . 2 Flowchart of genetic-algorithm-SVM-feature-screen (GA-SVM-FS) based on genetic algorithm and support vector machine (SVM). Figure 3 . 3 Gene encoding of three types of chromosomes.  ... 
doi:10.3390/en11081922 fatcat:dmjptizlifhivonta57lbqb6gi

GA Algorithm Optimizing SVM Multi-Class Kernel Parameters Applied in Arabic Speech Recognition

Aymen Mnassri, Mohammed Bennasr, Adnane Cherif
2017 Indian Journal of Science and Technology  
Objectives: This paper proposes a novel recognition technique (ASR) based on GA optimized SVM multi-class algorithm.  ...  Each word of them is extracted by Mel Frequency Cepstral Coefficients (MFCCs) and used as an input to the SVM multi-class classifier.  ...  Algorithm Genetic Algorithms (GA) represent a rather rich and interesting family of stochastic optimization algorithms based on the mechanisms of natural selection and genetics.  ... 
doi:10.17485/ijst/2017/v10i27/114943 fatcat:zprzl2vubvbulot7xcwktgj76a

Co-ABC: Correlation artificial bee colony algorithm for biomarker gene discovery using gene expression profile

Hala Mohammed Alshamlan
2018 Saudi Journal of Biological Sciences  
In this paper, we propose a new hybrid method based on Correlation-based feature selection method and Artificial Bee Colony algorithm,namely Co-ABC to select a small number of relevant genes for accurate  ...  The overall performance of our proposed Co-ABC algorithm was evaluated using six gene expression profile for binary and multi-class cancer datasets.  ...  Acknowledgment This research project was supported by a grant from the Research Center of the Center for Female Scientific and Medical Colleges Deanship of Scientific Research, King Saud University.  ... 
doi:10.1016/j.sjbs.2017.12.012 pmid:30108438 pmcid:PMC6088113 fatcat:72lw6gif3zda5juuzmwkp742na

Multi-class computational evolution: development, benchmark evaluation and application to RNA-Seq biomarker discovery

Nathaniel M. Crabtree, Jason H. Moore, John F. Bowyer, Nysia I. George
2017 BioData Mining  
Results: The multi-class CES was compared to three common feature selection and classification algorithms: support vector machine (SVM), random k-nearest neighbor (RKNN), and random forest (RF).  ...  The algorithms were evaluated on three distinct multi-class RNA sequencing datasets.  ...  Hill of Dartmouth College for his help in debugging the multi-class CES, and in developing the Pareto domination tournament for the multi-class implementation.  ... 
doi:10.1186/s13040-017-0134-8 pmid:28450890 pmcid:PMC5404302 fatcat:t2g5uq4jovbsri4ni4ocfca2uu

Basic Gene Discretization-Model Using Correlation Clustering For Distributed DNA Databases

Dr.Vijay Arputharaj J, Ms.Pushpa Rega Ganesan, Mr.Ponsuresh Manoharan, Ms.P. Supraja
2020 International journal of advanced networking and applications  
Finally MLRC algorithm is applied as classification algorithm to identify class labels of test genes sequences in a big dataset.  ...  It recognizes gene expressions by framing association rules in accordance with support measure and confidence measure on the input data set.It will extract and filter required data into clusters based  ...  Her wise academic advice and ideas have played an extremely important role in the work presented in this research. Without her support, this work would not have been possible.  ... 
doi:10.35444/ijana.2020.11055 fatcat:4j3jevytjncanl3v72n752gjwy
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