A New Evaluation Measure for Feature Subset Selection with Genetic Algorithm Article Swipe
Saptarsi Goswami
,
Sourav Saha
,
Subhayu Chakravorty
,
Amlan Chakrabarti
,
Basabi Chakraborty
·
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.5815/ijisa.2015.10.04
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.5815/ijisa.2015.10.04
Feature selection is one of the most important preprocessing steps for a data mining, pattern recognition or machine learning problem. Finding an optimal subset of features, among all the combinations is a NP-Complete problem. Lot of research has been done in feature selection. However, as the sizes of the datasets are increasing and optimality is a subjective notion, further research is needed to find better techniques. In this paper, a genetic algorithm based feature subset selection method has been proposed with a novel feature evaluation measure as the fitness function. The evaluation measure is different in three primary ways a) It considers the information content of the features apart from relevance with respect to the target b) The redundancy is considered only when it is over a threshold value c) There is lesser penalization towards cardinality of the subset. As the measure accepts value of few parameters, this is available for tuning as per the need of the particular problem domain. Experiments conducted over 21 well known publicly available datasets reveal superior performance. Hypothesis testing for the accuracy improvement is found to be statistically significant.
Related Topics
Concepts
Computer science
Feature selection
Cardinality (data modeling)
Measure (data warehouse)
Minimum redundancy feature selection
Preprocessor
Fitness function
Redundancy (engineering)
Feature (linguistics)
Data mining
Artificial intelligence
Pattern recognition (psychology)
Relevance (law)
Selection (genetic algorithm)
Data pre-processing
Machine learning
Genetic algorithm
Algorithm
Linguistics
Operating system
Philosophy
Law
Political science
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.5815/ijisa.2015.10.04
- http://www.mecs-press.org/ijisa/ijisa-v7-n10/IJISA-V7-N10-4.pdf
- OA Status
- bronze
- Cited By
- 9
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W1725582667
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W1725582667Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.5815/ijisa.2015.10.04Digital Object Identifier
- Title
-
A New Evaluation Measure for Feature Subset Selection with Genetic AlgorithmWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2015Year of publication
- Publication date
-
2015-09-08Full publication date if available
- Authors
-
Saptarsi Goswami, Sourav Saha, Subhayu Chakravorty, Amlan Chakrabarti, Basabi ChakrabortyList of authors in order
- Landing page
-
https://doi.org/10.5815/ijisa.2015.10.04Publisher landing page
- PDF URL
-
https://www.mecs-press.org/ijisa/ijisa-v7-n10/IJISA-V7-N10-4.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
-
https://www.mecs-press.org/ijisa/ijisa-v7-n10/IJISA-V7-N10-4.pdfDirect OA link when available
- Concepts
-
Computer science, Feature selection, Cardinality (data modeling), Measure (data warehouse), Minimum redundancy feature selection, Preprocessor, Fitness function, Redundancy (engineering), Feature (linguistics), Data mining, Artificial intelligence, Pattern recognition (psychology), Relevance (law), Selection (genetic algorithm), Data pre-processing, Machine learning, Genetic algorithm, Algorithm, Linguistics, Operating system, Philosophy, Law, Political scienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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9Total citation count in OpenAlex
- Citations by year (recent)
-
2023: 1, 2021: 1, 2019: 1, 2018: 3, 2017: 1Per-year citation counts (last 5 years)
- References (count)
-
25Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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