Optimal feature selection for classifying a large set of chemicals using metal oxide sensors

Sensors and Actuators B Chemical ◽

10.1016/j.snb.2013.01.088 ◽

2013 ◽

Vol 187 ◽

pp. 471-480 ◽

Author(s):

Thomas Nowotny ◽

Amalia Z. Berna ◽

Russell Binions ◽

Stephen Trowell

Keyword(s):

Feature Selection ◽

Metal Oxide ◽

Large Set ◽

Metal Oxide Sensors ◽

Optimal Feature Selection ◽

Selection For ◽

Optimal Feature

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Erratum to “Optimal feature selection for classifying a large set of chemicals using metal oxide sensors” [Sens. Actuators B Chem. 187 (2013) 471–480]

Sensors and Actuators B Chemical ◽

10.1016/j.snb.2013.10.083 ◽

2014 ◽

Vol 191 ◽

pp. 874-878

Author(s):

Thomas Nowotny ◽

Amalia Z. Berna ◽

Russell Binions ◽

Stephen Towell

Keyword(s):

Feature Selection ◽

Metal Oxide ◽

Large Set ◽

Metal Oxide Sensors ◽

Optimal Feature Selection ◽

Selection For ◽

Optimal Feature

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P1.0.21 Optimal Feature Selection for Classifying a Large Set of Chemicals Using Metal Oxide Sensors

10.5162/imcs2012/p1.0.21 ◽

2012 ◽

Author(s):

Thomas Nowotny ◽

Amalia Z. Berna ◽

Stephen Trowell ◽

Russell Binions

Keyword(s):

Feature Selection ◽

Metal Oxide ◽

Large Set ◽

Metal Oxide Sensors ◽

Optimal Feature Selection ◽

Selection For ◽

Optimal Feature

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Optimal feature selection for machine learning based intrusion detection system by exploiting attribute dependence

Materials Today Proceedings ◽

10.1016/j.matpr.2021.04.643 ◽

2021 ◽

Author(s):

Ghanshyam Prasad Dubey ◽

Dr. Rakesh Kumar Bhujade

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Intrusion Detection ◽

Intrusion Detection System ◽

Detection System ◽

Optimal Feature Selection ◽

Selection For ◽

Optimal Feature

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Ensemble Learning Classifier with Optimal Feature Selection for Parkinson's Disease

2020 International Conference on Advances in Computing, Communication & Materials (ICACCM) ◽

10.1109/icaccm50413.2020.9212961 ◽

2020 ◽

Author(s):

Preeti Khera ◽

Neelesh Kumar

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Feature Selection ◽

Ensemble Learning ◽

Learning Classifier ◽

Optimal Feature Selection ◽

Selection For ◽

Optimal Feature

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Optimal Feature Selection for Decision Trees Induction Using a Genetic Algorithm Wrapper - A Model Approach

Strategic Innovative Marketing and Tourism - Springer Proceedings in Business and Economics ◽

10.1007/978-3-030-36126-6_65 ◽

2020 ◽

pp. 583-591

Author(s):

Prokopis K. Theodoridis ◽

Dimitris C. Gkikas

Keyword(s):

Genetic Algorithm ◽

Feature Selection ◽

Decision Trees ◽

Optimal Feature Selection ◽

Selection For ◽

Optimal Feature ◽

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Optimal Feature Selection for Robust Classification via l2,1-Norms Regularization

2014 22nd International Conference on Pattern Recognition ◽

10.1109/icpr.2014.99 ◽

2014 ◽

Author(s):

Jiajun Wen ◽

Zhihui Lai ◽

Wai Keung Wong ◽

Jinrong Cui ◽

Minghua Wan

Keyword(s):

Feature Selection ◽

Robust Classification ◽

Optimal Feature Selection ◽

Selection For ◽

Optimal Feature

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Optimal Feature Selection for EMG-Based Finger Force Estimation Using LightGBM Model

2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN) ◽

10.1109/ro-man46459.2019.8956453 ◽

2019 ◽

Author(s):

Yuhang Ye ◽

Chao Liu ◽

Nabil Zemiti ◽

Chenguang Yang

Keyword(s):

Feature Selection ◽

Finger Force ◽

Force Estimation ◽

Optimal Feature Selection ◽

Selection For ◽

Optimal Feature

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Optimal Feature Selection for Learning-Based Algorithms for Sentiment Classification

Cognitive Computation ◽

10.1007/s12559-019-09669-5 ◽

2019 ◽

Vol 12 (1) ◽

pp. 238-248 ◽

Author(s):

Zhaoxia Wang ◽

Zhiping Lin

Keyword(s):

Feature Selection ◽

Sentiment Classification ◽

Optimal Feature Selection ◽

Selection For ◽

Optimal Feature

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Robust Optimal Feature Selection for Lung Tumor Recurrence Prediction in PET Imaging

International Journal of Radiation Oncology*Biology*Physics ◽

10.1016/j.ijrobp.2015.07.021 ◽

2015 ◽

Vol 93 (3) ◽

pp. S6

Author(s):

S. Ruan ◽

H. Mi ◽

C. Petitjean ◽

H. Li ◽

H.C. Chen ◽

...

Keyword(s):

Feature Selection ◽

Pet Imaging ◽

Tumor Recurrence ◽

Recurrence Prediction ◽

Optimal Feature Selection ◽

Selection For ◽

Optimal Feature

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Optimal feature selection for partial discharge recognition of cable systems based on the random forest method

2016 China International Conference on Electricity Distribution (CICED) ◽

10.1109/ciced.2016.7576360 ◽

2016 ◽

Author(s):

Xiaosheng Peng ◽

Guangyao Yang ◽

Shijie Zheng ◽

Lei Xiong ◽

Junyang Bai

Keyword(s):

Feature Selection ◽

Random Forest ◽

Partial Discharge ◽

Random Forest Method ◽

Cable Systems ◽

Optimal Feature Selection ◽

Selection For ◽

Optimal Feature

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