Supervised Feature Selection based on the Law of Total Variance Article Swipe
Nur Atiqah Mustapa
,
Azlyna Senawi
,
Hua‐Liang Wei
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.15282/mekatronika.v5i2.9998
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.15282/mekatronika.v5i2.9998
Feature selection is a fundamental pre-processing step in machine learning that decreases data dimensionality by removing superfluous and irrelevant features. This study proposes a supervised feature selection method based on feature relevance by employing the law of total variance (LTV). Specifically, the LTV is used to quantify the relevance of features by analysing the association between features and class label. Six classifiers were employed to evaluate the performance and reliability of the proposed method pertaining to classification accuracy. The results proved that a feature subset given by the proposed method has the capability to achieve comparable classification accuracy to the full feature set when just half or less than half of the original features are retained. The proposed method was also proven to be versatile as it can achieves adequate classification accuracy with all six classifiers with different learning schemes. In addition, a comparison with a similar type of feature selection method (AmRMR) shows that the proposed method yields a more accurate classification.
Related Topics
Concepts
Feature selection
Artificial intelligence
Feature (linguistics)
Pattern recognition (psychology)
Relevance (law)
Computer science
Variance (accounting)
Curse of dimensionality
Selection (genetic algorithm)
Set (abstract data type)
Data mining
Machine learning
Linguistics
Business
Accounting
Philosophy
Programming language
Political science
Law
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.15282/mekatronika.v5i2.9998
- https://journal.ump.edu.my/mekatronika/article/download/9998/3083
- OA Status
- diamond
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4391077545
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4391077545Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.15282/mekatronika.v5i2.9998Digital Object Identifier
- Title
-
Supervised Feature Selection based on the Law of Total VarianceWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-12-28Full publication date if available
- Authors
-
Nur Atiqah Mustapa, Azlyna Senawi, Hua‐Liang WeiList of authors in order
- Landing page
-
https://doi.org/10.15282/mekatronika.v5i2.9998Publisher landing page
- PDF URL
-
https://journal.ump.edu.my/mekatronika/article/download/9998/3083Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://journal.ump.edu.my/mekatronika/article/download/9998/3083Direct OA link when available
- Concepts
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Feature selection, Artificial intelligence, Feature (linguistics), Pattern recognition (psychology), Relevance (law), Computer science, Variance (accounting), Curse of dimensionality, Selection (genetic algorithm), Set (abstract data type), Data mining, Machine learning, Linguistics, Business, Accounting, Philosophy, Programming language, Political science, LawTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
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25Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.half | 105, 109 |
| abstract_inverted_index.just | 104 |
| abstract_inverted_index.less | 107 |
| abstract_inverted_index.more | 160 |
| abstract_inverted_index.step | 6 |
| abstract_inverted_index.than | 108 |
| abstract_inverted_index.that | 10, 81, 154 |
| abstract_inverted_index.type | 147 |
| abstract_inverted_index.used | 44 |
| abstract_inverted_index.were | 62 |
| abstract_inverted_index.when | 103 |
| abstract_inverted_index.with | 132, 136, 144 |
| abstract_inverted_index.based | 28 |
| abstract_inverted_index.class | 58 |
| abstract_inverted_index.given | 85 |
| abstract_inverted_index.shows | 153 |
| abstract_inverted_index.study | 21 |
| abstract_inverted_index.total | 37 |
| abstract_inverted_index.(LTV). | 39 |
| abstract_inverted_index.label. | 59 |
| abstract_inverted_index.method | 27, 73, 89, 118, 151, 157 |
| abstract_inverted_index.proved | 80 |
| abstract_inverted_index.proven | 121 |
| abstract_inverted_index.subset | 84 |
| abstract_inverted_index.yields | 158 |
| abstract_inverted_index.(AmRMR) | 152 |
| abstract_inverted_index.Feature | 0 |
| abstract_inverted_index.achieve | 94 |
| abstract_inverted_index.between | 55 |
| abstract_inverted_index.feature | 25, 30, 83, 101, 149 |
| abstract_inverted_index.machine | 8 |
| abstract_inverted_index.results | 79 |
| abstract_inverted_index.similar | 146 |
| abstract_inverted_index.accuracy | 97, 131 |
| abstract_inverted_index.accurate | 161 |
| abstract_inverted_index.achieves | 128 |
| abstract_inverted_index.adequate | 129 |
| abstract_inverted_index.employed | 63 |
| abstract_inverted_index.evaluate | 65 |
| abstract_inverted_index.features | 50, 56, 113 |
| abstract_inverted_index.learning | 9, 138 |
| abstract_inverted_index.original | 112 |
| abstract_inverted_index.proposed | 72, 88, 117, 156 |
| abstract_inverted_index.proposes | 22 |
| abstract_inverted_index.quantify | 46 |
| abstract_inverted_index.removing | 15 |
| abstract_inverted_index.schemes. | 139 |
| abstract_inverted_index.variance | 38 |
| abstract_inverted_index.accuracy. | 77 |
| abstract_inverted_index.addition, | 141 |
| abstract_inverted_index.analysing | 52 |
| abstract_inverted_index.decreases | 11 |
| abstract_inverted_index.different | 137 |
| abstract_inverted_index.employing | 33 |
| abstract_inverted_index.features. | 19 |
| abstract_inverted_index.relevance | 31, 48 |
| abstract_inverted_index.retained. | 115 |
| abstract_inverted_index.selection | 1, 26, 150 |
| abstract_inverted_index.versatile | 124 |
| abstract_inverted_index.capability | 92 |
| abstract_inverted_index.comparable | 95 |
| abstract_inverted_index.comparison | 143 |
| abstract_inverted_index.irrelevant | 18 |
| abstract_inverted_index.pertaining | 74 |
| abstract_inverted_index.supervised | 24 |
| abstract_inverted_index.association | 54 |
| abstract_inverted_index.classifiers | 61, 135 |
| abstract_inverted_index.fundamental | 4 |
| abstract_inverted_index.performance | 67 |
| abstract_inverted_index.reliability | 69 |
| abstract_inverted_index.superfluous | 16 |
| abstract_inverted_index.Specifically, | 40 |
| abstract_inverted_index.classification | 76, 96, 130 |
| abstract_inverted_index.dimensionality | 13 |
| abstract_inverted_index.pre-processing | 5 |
| abstract_inverted_index.classification. | 162 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.7599999904632568 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile.value | 0.22140592 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |