Opposition Based Firefly Algorithm Optimized Feature Subset Selection Approach for Fetal Risk Anticipation

Subha V; Murugan D

doi:10.5121/mlaij.2016.3205

Opposition Based Firefly Algorithm Optimized Feature Subset Selection Approach for Fetal Risk Anticipation

Machine Learning and Applications An International Journal ◽

10.5121/mlaij.2016.3205 ◽

2016 ◽

Vol 3 (2) ◽

pp. 55-64 ◽

Cited By ~ 1

Author(s):

Subha V ◽

Murugan D

Keyword(s):

Firefly Algorithm ◽

Subset Selection ◽

Feature Subset Selection ◽

Feature Subset ◽

Fetal Risk ◽

Selection Approach

Download Full-text

A new metaheuristic feature subset selection approach for image steganalysis

Journal of Intelligent & Fuzzy Systems ◽

10.3233/ifs-131111 ◽

2014 ◽

Vol 27 (3) ◽

pp. 1445-1455 ◽

Cited By ~ 5

Author(s):

Farid Ghareh Mohammadi ◽

Mohammad Saniee Abadeh

Keyword(s):

Subset Selection ◽

Feature Subset Selection ◽

Feature Subset ◽

Image Steganalysis ◽

Selection Approach

Download Full-text

Feature Subset Selection Approach by Gray-Wolf Optimization

Advances in Intelligent Systems and Computing - Afro-European Conference for Industrial Advancement ◽

10.1007/978-3-319-13572-4_1 ◽

2015 ◽

pp. 1-13 ◽

Cited By ~ 60

Author(s):

E. Emary ◽

Hossam M. Zawbaa ◽

Crina Grosan ◽

Abul Ella Hassenian

Keyword(s):

Subset Selection ◽

Feature Subset Selection ◽

Gray Wolf ◽

Feature Subset ◽

Selection Approach

Download Full-text

Extreme Learning Machine-based Differential Evolution Feature Selection Approach for Feature Subset Selection from Digital Images

International Journal of Applied Research on Information Technology and Computing ◽

10.5958/0975-8089.2016.00013.0 ◽

2016 ◽

Vol 7 (2) ◽

pp. 119

Author(s):

P.T.T. Bharathi ◽

P. Subashini ◽

G.K. Asha

Keyword(s):

Feature Selection ◽

Differential Evolution ◽

Extreme Learning Machine ◽

Subset Selection ◽

Feature Subset Selection ◽

Feature Subset ◽

Selection Approach ◽

Evolution Feature ◽

Learning Machine ◽

Feature Selection Approach

Download Full-text

An efficient feature subset selection approach for machine learning

Multimedia Tools and Applications ◽

10.1007/s11042-020-10011-7 ◽

2021 ◽

Author(s):

Thomas Rincy N ◽

Roopam Gupta

Keyword(s):

Machine Learning ◽

Subset Selection ◽

Feature Subset Selection ◽

Feature Subset ◽

Selection Approach

Download Full-text

Hybrid Feature Subset Selection Approach for Fuzzy-Extreme Learning Machine

Data-Enabled Discovery and Applications ◽

10.1007/s41688-017-0011-0 ◽

2017 ◽

Vol 1 (1) ◽

Cited By ~ 1

Author(s):

A. Kale ◽

S. Sonavane

Keyword(s):

Extreme Learning Machine ◽

Subset Selection ◽

Feature Subset Selection ◽

Feature Subset ◽

Selection Approach ◽

Learning Machine

Download Full-text

Feature subset selection approach based on fuzzy rough set for high-dimensional data

2014 IEEE International Conference on Granular Computing (GrC) ◽

10.1109/grc.2014.6982810 ◽

2014 ◽

Cited By ~ 4

Author(s):

Changyou Guo ◽

Xuefeng Zheng

Keyword(s):

Rough Set ◽

High Dimensional Data ◽

Subset Selection ◽

Feature Subset Selection ◽

High Dimensional ◽

Feature Subset ◽

Fuzzy Rough Set ◽

Selection Approach

Download Full-text

Correction to: An efficient feature subset selection approach for machine learning

Multimedia Tools and Applications ◽

10.1007/s11042-021-11785-0 ◽

2021 ◽

Author(s):

Thomas Rincy N ◽

Roopam Gupta

Keyword(s):

Machine Learning ◽

Subset Selection ◽

Feature Subset Selection ◽

Feature Subset ◽

Selection Approach

Download Full-text

A Four-Stage Hybrid Feature Subset Selection Approach for Network Traffic Classification Based on Full Coverage

Security, Privacy, and Anonymity in Computation, Communication, and Storage - Lecture Notes in Computer Science ◽

10.1007/978-3-030-05345-1_15 ◽

2018 ◽

pp. 178-191

Author(s):

Jingbo Xia ◽

Jian Shen ◽

Yaoxiang Wu

Keyword(s):

Network Traffic ◽

Subset Selection ◽

Feature Subset Selection ◽

Feature Subset ◽

Traffic Classification ◽

Full Coverage ◽

Network Traffic Classification ◽

Selection Approach

Download Full-text

Empirical evaluation of feature subset selection based on a real-world data set

Engineering Applications of Artificial Intelligence ◽

10.1016/j.engappai.2004.03.005 ◽

2004 ◽

Vol 17 (3) ◽

pp. 285-288 ◽

Cited By ~ 5

Author(s):

Petra Perner ◽

Chid Apte

Keyword(s):

Real World ◽

Empirical Evaluation ◽

Subset Selection ◽

Feature Subset Selection ◽

Feature Subset ◽

Real World Data ◽

Data Set ◽

World Data

Download Full-text

Threshold accepting trained principal component neural network and feature subset selection: Application to bankruptcy prediction in banks

Applied Soft Computing ◽

10.1016/j.asoc.2007.12.003 ◽

2008 ◽

Vol 8 (4) ◽

pp. 1539-1548 ◽

Cited By ~ 67

Author(s):

V. Ravi ◽

C. Pramodh

Keyword(s):

Neural Network ◽

Subset Selection ◽

Principal Component ◽

Bankruptcy Prediction ◽

Feature Subset Selection ◽

Feature Subset ◽

Threshold Accepting

Download Full-text