Online citations, reference lists, and bibliographies.
Please confirm you are human
(Sign Up for free to never see this)
← Back to Search

A Novel Ensemble Of Classifiers For Microarray Data Classification

Y. Chen, Yaou Zhao
Published 2008 · Mathematics, Computer Science

Save to my Library
Download PDF
Analyze on Scholarcy
Share
Micorarray data are often extremely asymmetric in dimensionality, such as thousands or even tens of thousands of genes and a few hundreds of samples. Such extreme asymmetry between the dimensionality of genes and samples presents several challenges to conventional clustering and classification methods. In this paper, a novel ensemble method is proposed. Firstly, in order to extract useful features and reduce dimensionality, different feature selection methods such as correlation analysis, Fisher-ratio is used to form different feature subsets. Then a pool of candidate base classifiers is generated to learn the subsets which are re-sampling from the different feature subsets with PSO (Particle Swarm Optimization) algorithm. At last, appropriate classifiers are selected to construct the classification committee using EDAs (Estimation of Distribution Algorithms). Experiments show that the proposed method produces the best recognition rates on four benchmark databases.
This paper references
10.1142/S0129065705000396
Applications of support vector machines to cancer classification with microarray data
F. Chu (2005)
RBF neural network center selection based on Fisher ratio class separability measure , IEEE Trans
K. Z. Mao (2000)
Proceedings of the 8th International Workshop on Multiple Classifier Systems
J. Benediktsson (2009)
10.1142/S0218001402002015
Exploring Features and Classifiers to Classify Gene Expression Profiles of Acute Leukemia
S. Cho (2002)
10.1093/bioinformatics/16.10.906
Support vector machine classification and validation of cancer tissue samples using microarray expression data
T. Furey (2000)
10.1186/1471-2105-6-148
Feature selection and classification for microarray data analysis: Evolutionary methods for identifying predictive genes
Thanyaluk Jirapech-Umpai (2004)
10.1016/S1369-5274(00)00091-6
Monitoring gene expression using DNA microarrays.
C. A. Harrington (2000)
10.1093/bioinformatics/bti631
Simple decision rules for classifying human cancers from gene expression profiles
A. C. Tan (2005)
10.1145/1014052.1014149
Redundancy based feature selection for microarray data
Lei Yu (2004)
10.1016/0893-6080(89)90003-8
On the approximate realization of continuous mappings by neural networks
Ken-ichi Funahashi (1989)
10.1016/S0140-6736(02)07746-2
Use of proteomic patterns in serum to identify ovarian cancer
E. Petricoin (2002)
10.1109/ISEFS.2006.251144
Neuro-Fuzzy Ensemble Approach for Microarray Cancer Gene Expression Data Analysis
Z. Wang (2006)
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Pedro Larraanaga (2001)
10.1016/S0076-6879(99)03014-1
DNA arrays for analysis of gene expression.
M. Eisen (1999)
Translation of microarray data into clinically relevant cancer diagnostic tests using gene expression ratios in lung cancer and mesothelioma.
G. Gordon (2002)
10.1016/S0004-3702(02)00190-X
Ensembling neural networks: Many could be better than all
Z. Zhou (2002)
10.1126/SCIENCE.286.5439.531
Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.
T. Golub (1999)
10.1073/PNAS.96.12.6745
Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays.
U. Alon (1999)
10.1016/j.artmed.2005.06.002
The classification of cancer based on DNA microarray data that uses diverse ensemble genetic programming
J. Hong (2006)
10.1007/3-540-45014-9_1
Ensemble Methods in Machine Learning
Thomas G. Dietterich (2000)
10.1109/TNN.2002.1031953
RBF neural network center selection based on Fisher ratio class separability measure
K. Mao (2002)



This paper is referenced by
Incremental Radial Basis Function Computation for Neural Networks
V. Skala (2011)
10.1007/s10489-017-0982-4
An exploratory study of mono and multi-objective metaheuristics to ensemble of classifiers
Antonino Feitosa Neto (2017)
Recomendação automática da estrutura de comitês de classificadores usando meta-aprendizado
R. A. Silva (2020)
10.1108/EC-11-2015-0355
Using a grid computing-based meta-evolutionary mining approach for the microarray data cancer-categorization
Tai-Wei Chiang (2017)
10.1109/BIBM.2010.5706582
Multi-objective evolutionary algorithms based Interpretable Fuzzy models for microarray gene expression data analysis
Z. Wang (2010)
10.1016/j.eswa.2009.06.100
A hybrid immune-estimation distribution of algorithm for mining thyroid gland data
Wei-Wen Chang (2010)
10.1016/B978-0-12-804203-8.00015-8
Feature Selection and Classification of Microarray Data Using Machine Learning Techniques
M. Kumar (2016)
Novel computationally intelligent machine learning algorithms for data mining and knowledge discovery
I. A. Gheyas (2009)
10.4137/BBI.S2908
Gene Expression Based Leukemia Sub-Classification Using Committee Neural Networks
Mihir Sewak (2009)
10.1016/J.IMU.2017.07.004
Feature selection model based on clustering and ranking in pipeline for microarray data
B. Sahu (2017)
Mutual Information and Cross Entropy Framework to Determine Relevant Gene Subset for Cancer Classification
R. Bala (2011)
10.4018/978-1-4666-6078-6.CH004
Towards an Improved Ensemble Learning Model of Artificial Neural Networks: Lessons Learned on Using Randomized Numbers of Hidden Neurons
F. Anifowose (2014)
10.4238/2012.May.15.6
Multiclass microarray data classification based on confidence evaluation.
H. Yu (2012)
10.1109/TCBB.2018.2868341
Classification of a DNA Microarray for Diagnosing Cancer Using a Complex Network Based Method
P. Wu (2019)
10.1109/BIBM.2013.6732720
Studying the robustness of ensembles of classifiers used for cancer diagnosis using microarrays datasets
Mohammed A. Gaafar (2013)
10.1016/j.ins.2012.02.009
Novel swarm optimization for mining classification rules on thyroid gland data
W. Yeh (2012)
Prediction of Cancer Subtypes using Fuzzy Hypersphere Clustering Neural Network
U. V. Kulkarni (2011)
10.3901/CJME.2012.04.665
Quadratic programming-based approach for autonomous vehicle path planning in space
Yang Chen (2012)
10.1007/978-981-4585-18-7_44
A Comparative Study of Cancer Classification Methods Using Microarray Gene Expression Profile
Hala M. Alshamlan (2013)
10.3390/info9110268
Hybrid Metaheuristics to the Automatic Selection of Features and Members of Classifier Ensembles
Antonino Feitosa Neto (2018)
10.1109/IJCNN.2009.5178942
Impact of multiple clusters on neural classification of ROIs in digital mammograms
B. Verma (2009)
10.1016/j.asoc.2014.01.002
A novel ensemble of classifiers that use biological relevant gene sets for microarray classification
M. Reboiro-Jato (2014)
International Journal of Recent Trends in Engineering , Vol 2 , No . 5 , November 2009 8 Ensemble Voting System for Anomaly Based Network Intrusion Detection
M. Panda (2009)
10.1007/978-3-642-40319-4_7
Ensemble Learning Model for Petroleum Reservoir Characterization: A Case of Feed-Forward Back-Propagation Neural Networks
Anifowose Fatai (2013)
10.1109/SPAC46244.2018.8965553
Comparison of Different Classification Methods for Breast Cancer Subtypes Prediction
J. Xu (2018)
10.1007/978-3-319-63312-1_66
Classifying DNA Microarray for Cancer Diagnosis via Method Based on Complex Networks
P. Wu (2017)
10.1016/j.asoc.2014.10.017
Improving the prediction of petroleum reservoir characterization with a stacked generalization ensemble model of support vector machines
Anifowose Fatai (2015)
CSI 5180. Topics in Artificial Intelligence Machine Learning for Bioinformatics Applications
Marcel Turcotte (2019)
An Experimental Comparison of Classifier Combining Methods Using Artificial Data
Fuad M. Alkoot (2019)
10.4103/2228-7477.143811
Cancer Classification in Microarray Data using a Hybrid Selective Independent Component Analysis and υ-Support Vector Machine Algorithm
H. Saberkari (2014)
10.1109/ICTAI.2013.64
A Review of Ensemble Classification for DNA Microarrays Data
T. Khoshgoftaar (2013)
10.1088/1742-6596/1613/1/012064
Ensemble-support vector machine-random undersampling: Simulation study of multiclass classification for handling high dimensional and imbalanced data
Nur Silviyah Rahmi (2020)
See more
Semantic Scholar Logo Some data provided by SemanticScholar