4 Hits in 2.5 sec

A Review of Ensemble Methods in Bioinformatics

Pengyi Yang, Yee Hwa Yang, Bing B. Zhou, Albert Y. Zomaya
2010 Current Bioinformatics  
First, it is to provide a review of the most widely used ensemble learning methods and their application in various bioinformatics problems, including the main topics of gene expression, mass spectrometry-based  ...  Recent work in computational biology has seen an increasing use of ensemble learning methods due to their unique advantages in dealing with small sample size, high-dimensionality, and complexity data structures  ...  We also thank Georgina Wilcox for editing the article. Pengyi Yang is supported by the NICTA International Postgraduate Award (NIPA) and the NICTA Research Project Award (NRPA).  ... 
doi:10.2174/157489310794072508 fatcat:muzcldjxifc23kl4tynz4lwjlu

Wavelet feature extraction for high-dimensional microarray data

Yihui Liu
2009 Neurocomputing  
A novel method of feature extraction and dimensionality reduction for high-dimensional microarray data is proposed in this study.  ...  Therefore, dimensionality reduction is an important strategy to greatly improve the classification performance of microarray data.  ...  Acknowledgments This study is supported by research funds of Shandong Institute of Light Industry (12041653), and by International Collaboration Project of Shandong Province Education Department, China  ... 
doi:10.1016/j.neucom.2008.04.010 fatcat:7kbwpg2rmbhyvbiglz3hayrlcq

Meta-classifiers for high-dimensional, small sample classification for gene expression analysis

Kyung-Joong Kim, Sung-Bae Cho
2014 Pattern Analysis and Applications  
In the previous works, researchers show that evolutionary computation is useful to build an ensemble from the pairs of feature selection and classification algorithms.  ...  Classification using small sample size (limited number of samples) with high dimension is a challenging problem in both machine learning and medicine as there are a wide variety of possible modeling approaches  ...  [14] used the random forest (an ensemble of trees) to classify gene expression datasets. This showed comparable performance to other classification methods (DLDA, KNN, and SVM).  ... 
doi:10.1007/s10044-014-0369-7 fatcat:gdrihmdn5zc6vgj2g7r67pu3m4

Ensembles based on Random Projection for gene expression data analysis [article]

In the second part of the work, we propose ensemble algorithms based on Random Subspaces and Random Projections, and we experimentally compare them with single SVM and other state-of-the-art ensemble methods  ...  In this work we propose and we experimentally analyze two ensemble methods based on two randomized techniques for data compression: Random Subspaces and Random Projections.  ...  Acknowledgments Thanks also to all people, including my Relator and Co-relator, who have backed me up during these years. 157 .  ... 
doi:10.13130/folgieri-raffaella_phd2008 fatcat:rb7o42aasnazplxsg2j3zyrg3q