Improvised Feature Subset Selection Algorithm (FAST) for High Dimensional Data
Priyanka Mate
, G.P. Chakote
Feature subset selection Feature clustering Filter method
In choosing a feature, we are concerned about finding those features that produce results similar to the original set of features. We take efficiency and effectiveness into consideration while evaluating the feature selection algorithm. Efficiency in dealing with the time needed to find a subset of features and performance with the quality of a subset of features. These criteria have introduced the FAST (FAST) Advanced Feature Selection Grouping and have been evaluated and used in this document. Reducing the size of data is one of FAST's most important features. First, we use group-graphing theories to segment properties. We then create a subset of features by selecting the most representative features and most relevant to the target classes. Because of the features in the groups are quite independent. FAST's grouping strategy is highly likely to provide a subset of useful and independent features. Specifies a subset of the most useful features that produce a compatible result because all feature sets are involved in the feature selection. The attribute selection algorithm can be evaluated from the performance point of view and its effectiveness. Performance is related to the quality of a subset of features, performance relative to the time it takes to find a subset of features.
"Improvised Feature Subset Selection Algorithm (FAST) for High Dimensional Data", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.3, Issue 5, page no.295 - 299, May-2018, Available :https://ijsdr.org/papers/IJSDR1805041.pdf
Volume 3
Issue 5,
May-2018
Pages : 295 - 299
Paper Reg. ID: IJSDR_180227
Published Paper Id: IJSDR1805041
Downloads: 000347226
Research Area: Engineering
Country: Aurangabad, Maharashtra, 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
