Clustering of Interval Valued Data Through Interval Valued Feature Selection: Filter Based Approaches

Mining Intelligence and Knowledge Exploration - Lecture Notes in Computer Science ◽

10.1007/978-3-030-66187-8_26 ◽

2020 ◽

pp. 270-285

Author(s):

D. S. Guru ◽

N. Vinay Kumar

Keyword(s):

Feature Selection ◽

Interval Valued

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Feature selection for interval-valued data based on D-S evidence theory

IEEE Access ◽

10.1109/access.2021.3109013 ◽

2021 ◽

pp. 1-1

Author(s):

Yichun Peng ◽

Qinli Zhang

Keyword(s):

Feature Selection ◽

Evidence Theory ◽

Selection For ◽

Interval Valued

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Novel feature ranking criteria for interval valued feature selection

2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI) ◽

10.1109/icacci.2016.7732039 ◽

2016 ◽

Author(s):

D S Guru ◽

N Vinay Kumar

Keyword(s):

Feature Selection ◽

Feature Ranking ◽

Ranking Criteria ◽

Interval Valued

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Feature selection for dynamic interval-valued ordered data based on fuzzy dominance neighborhood rough set

Knowledge-Based Systems ◽

10.1016/j.knosys.2021.107223 ◽

2021 ◽

pp. 107223

Author(s):

Binbin Sang ◽

Hongmei Chen ◽

Lei Yang ◽

Tianrui Li ◽

Weihua Xu ◽

...

Keyword(s):

Feature Selection ◽

Rough Set ◽

Neighborhood Rough Set ◽

Selection For ◽

Ordered Data ◽

Interval Valued

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Interval-Valued Feature Selection for Classification of Text Documents

Advances in Intelligent Systems and Computing - Intelligent Systems Design and Applications ◽

10.1007/978-3-030-71187-0_95 ◽

2021 ◽

pp. 1028-1038

Author(s):

N. Vinay Kumar ◽

K. Swarnalatha ◽

D. S. Guru ◽

B. S. Anami

Keyword(s):

Feature Selection ◽

Text Documents ◽

Selection For ◽

Interval Valued

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A Novel Feature Ranking Criterion for Supervised Interval Valued Feature Selection for Classification

2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) ◽

10.1109/icdar.2017.331 ◽

2017 ◽

Author(s):

N. Vinay Kumar ◽

D.S. Guru

Keyword(s):

Feature Selection ◽

Feature Ranking ◽

Ranking Criterion ◽

Selection For ◽

Interval Valued

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Class Specific Feature Selection for Interval Valued Data Through Interval K-Means Clustering

Communications in Computer and Information Science - Recent Trends in Image Processing and Pattern Recognition ◽

10.1007/978-981-10-4859-3_21 ◽

2017 ◽

pp. 228-239 ◽

Author(s):

D. S. Guru ◽

N. Vinay Kumar

Keyword(s):

Feature Selection ◽

Selection For ◽

Interval Valued

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Interval Valued Feature Selection for Classification of Logo Images

Advances in Intelligent Systems and Computing - Intelligent Systems Design and Applications ◽

10.1007/978-3-319-76348-4_16 ◽

2018 ◽

pp. 154-165 ◽

Author(s):

D. S. Guru ◽

N. Vinay Kumar

Keyword(s):

Feature Selection ◽

Selection For ◽

Interval Valued

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Interval Chi-Square Score (ICSS): Feature Selection of Interval Valued Data

Advances in Intelligent Systems and Computing - Intelligent Systems Design and Applications ◽

10.1007/978-3-030-16660-1_67 ◽

2019 ◽

pp. 686-698

Author(s):

D. S. Guru ◽

N. Vinay Kumar

Keyword(s):

Feature Selection ◽

Interval Valued ◽

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Interval-valued fuzzy-rough feature selection in datasets with missing values

2009 IEEE International Conference on Fuzzy Systems ◽

10.1109/fuzzy.2009.5277289 ◽

2009 ◽

Author(s):

Richard Jensen ◽

Qiang Shen

Keyword(s):

Feature Selection ◽

Missing Values ◽

Interval Valued

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Recognition Task with Feature Selection and Weighted Majority Voting Based on Interval-Valued Fuzzy Sets

Computational Collective Intelligence. Technologies and Applications - Lecture Notes in Computer Science ◽

10.1007/978-3-642-34630-9_21 ◽

2012 ◽

pp. 204-209 ◽

Author(s):

Robert Burduk

Keyword(s):

Feature Selection ◽

Recognition Task ◽

Majority Voting ◽

Weighted Majority ◽

Weighted Majority Voting ◽

Interval Valued

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