A Survey on Boosting High dimensional Feature Selection Classification
kokilamani.E
, Gunavathi.R
Optimal Linear Programming Boosting,Prediction, Estimate, Misclassification, Feature Selection
Classification problems in high dimensional data with small number of observations are becoming more common particularly in microarray data. Throughout the last two decades, plenty of efficient categorization models and feature selection (FS) algorithms have been planned for high prediction accuracies. The optimal Linear Programming Boosting (LPBoost) is a supervise classifier since the boosting family of classifiers. To predict or the feature selection (FS) algorithm applied is not efficient with the accurate data set. The LP Boost maximizes a margin between training samples of dissimilar classes and therefore also belongs to the class of margin-maximizing supervised classification algorithms. Therefore, Booster can also be used as a criterion to estimate the act of an FS algorithm or to estimate the complexity of a data set for classification. LPBoost iteratively optimizes double misclassification costs and vigorously generates pathetic hypotheses to build new LP columns.
" A Survey on Boosting High dimensional Feature Selection Classification", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.1, Issue 9, page no.220 - 222, September-2016, Available :https://ijsdr.org/papers/IJSDR1609033.pdf
Volume 1
Issue 9,
September-2016
Pages : 220 - 222
Paper Reg. ID: IJSDR_160799
Published Paper Id: IJSDR1609033
Downloads: 000347026
Research Area: Engineering
Country: udumalpet, Tiruppur, Tamilnadu, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave