Feature Transformation Through Stacked Autoencoder for Diabetes Classification

Lecture Notes in Electrical Engineering - Proceedings of the 12th National Technical Seminar on Unmanned System Technology 2020 ◽

10.1007/978-981-16-2406-3_32 ◽

2021 ◽

pp. 421-433

Author(s):

Aravind Vivekanandan ◽

Arun Kumar Shanmugam

Keyword(s):

Feature Transformation ◽

Stacked Autoencoder

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High-Voltage Circuit Breaker Fault Diagnosis Using a Hybrid Feature Transformation Approach Based on Random Forest and Stacked Autoencoder

IEEE Transactions on Industrial Electronics ◽

10.1109/tie.2018.2879308 ◽

2019 ◽

Vol 66 (12) ◽

pp. 9777-9788 ◽

Author(s):

Suliang Ma ◽

Mingxuan Chen ◽

Jianwen Wu ◽

Yuhao Wang ◽

Bowen Jia ◽

...

Keyword(s):

Fault Diagnosis ◽

Random Forest ◽

High Voltage ◽

Circuit Breaker ◽

Feature Transformation ◽

High Voltage Circuit Breaker ◽

Stacked Autoencoder ◽

Transformation Approach

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An Active Feature Transformation Method for Attitude Recognition of Video Bloggers

10.21437/interspeech.2018-1222 ◽

2018 ◽

Author(s):

Fasih Haider ◽

Fahim A. Salim ◽

Owen Conlan ◽

Saturnino Luz

Keyword(s):

Transformation Method ◽

Feature Transformation

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Construction of prior models for ES-MDA by a deep neural network with a stacked autoencoder for predicting reservoir production

Journal of Petroleum Science and Engineering ◽

10.1016/j.petrol.2019.106800 ◽

2020 ◽

Vol 187 ◽

pp. 106800 ◽

Author(s):

Jaejun Kim ◽

Sungil Kim ◽

Changhyup Park ◽

Kyungbook Lee

Keyword(s):

Neural Network ◽

Deep Neural Network ◽

Stacked Autoencoder ◽

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Classification of Liver Cancer Subtypes Based on Hierarchical Integrated Stacked Autoencoder

2020 6th International Conference on Robotics and Artificial Intelligence ◽

10.1145/3449301.3449316 ◽

2020 ◽

Author(s):

Tiantian Zhang ◽

Shuxu Zhao ◽

Zhaoping Zhang

Keyword(s):

Liver Cancer ◽

Cancer Subtypes ◽

Stacked Autoencoder

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Fault Diagnosis for Power Converters based on Random Forests and Feature Transformation

2020 IEEE 9th International Power Electronics and Motion Control Conference (IPEMC2020-ECCE Asia) ◽

10.1109/ipemc-ecceasia48364.2020.9367970 ◽

2020 ◽

Author(s):

Lei Kou ◽

Chuang Liu ◽

Guo-wei Cai ◽

Zhe Zhang ◽

Xue-jiao Li ◽

...

Keyword(s):

Fault Diagnosis ◽

Random Forests ◽

Power Converters ◽

Feature Transformation

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Anomaly detection of organic pollution in drinking water based on fluorescence spectroscopy and stacked autoencoder

2020 Chinese Automation Congress (CAC) ◽

10.1109/cac51589.2020.9327511 ◽

2020 ◽

Author(s):

Jiegen Shi ◽

Yitong Cao ◽

Jie Yu ◽

Dibo Hou ◽

Yongqin Ren ◽

...

Keyword(s):

Drinking Water ◽

Fluorescence Spectroscopy ◽

Anomaly Detection ◽

Organic Pollution ◽

Stacked Autoencoder ◽

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New class-dependent feature transformation for intrusion detection systems

Security and Communication Networks ◽

10.1002/sec.403 ◽

2011 ◽

Vol 5 (12) ◽

pp. 1296-1311 ◽

Author(s):

Mehdi Mohammadi ◽

Bijan Raahemi ◽

Ahmad Akbari ◽

Babak Nassersharif

Keyword(s):

Intrusion Detection ◽

Intrusion Detection Systems ◽

Feature Transformation ◽

Detection Systems ◽

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Feature Transformation And Subset Selection

IEEE Intelligent Systems and their Applications ◽

10.1109/mis.1998.671088 ◽

1998 ◽

Vol 13 (2) ◽

pp. 26-28 ◽

Author(s):

Huan Liu ◽

H. Motoda

Keyword(s):

Subset Selection ◽

Feature Transformation

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A feature transformation method using multiobjective genetic programming for two-class classification

2015 IEEE Congress on Evolutionary Computation (CEC) ◽

10.1109/cec.2015.7257261 ◽

2015 ◽

Author(s):

Tomoyuki Hiroyasu ◽

Toshihide Shiraishi ◽

Tomoya Yoshida

Keyword(s):

Genetic Programming ◽

Transformation Method ◽

Feature Transformation

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A Novel Double-Stacked Autoencoder for Power Transformers DGA Signals with Imbalanced Data Structure

IEEE Transactions on Industrial Electronics ◽

10.1109/tie.2021.3059543 ◽

2021 ◽

pp. 1-1

Author(s):

Dongsheng Yang ◽

Jia Qin ◽

Yongheng Pang ◽

Tingwen Huang

Keyword(s):

Data Structure ◽

Imbalanced Data ◽

Power Transformers ◽

Stacked Autoencoder

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