An optimized feature selection technique based on incremental feature analysis for bio-metric gait data classification

2016 ◽  
Vol 76 (22) ◽  
pp. 24457-24475 ◽  
Author(s):  
Vijay Bhaskar Semwal ◽  
Joyeeta Singha ◽  
Pinki Kumari Sharma ◽  
Arun Chauhan ◽  
Basudeba Behera
Keyword(s):  
Feature Selection ◽  
Data Classification ◽  
Feature Analysis ◽  
Feature Selection Technique ◽  
Selection Technique

Identification of Secretory Proteins of Malaria Parasite by Feature Selection Technique

2017 ◽  
Vol 14 (9) ◽  
Author(s):  
Hua Tang ◽  
Chunmei Zhang ◽  
Rong Chen ◽  
Po Huang ◽  
Chenggang Duan ◽  
...  
Keyword(s):  
Feature Selection ◽  
Malaria Parasite ◽  
Secretory Proteins ◽  
Feature Selection Technique ◽  
Selection Technique

An intelligent approach towards very short-term load forecasting

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Uttamarani Pati ◽  
Papia Ray ◽  
Arvind R. Singh
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Load Forecasting ◽  
Dew Point ◽  
Short Term ◽  
Spinning Reserve ◽  
Feature Selection Technique ◽  
Selection Technique ◽  
Load Demand ◽  
Short Term Load Forecasting

Abstract Very short term load forecasting (VSTLF) plays a pivotal role in helping the utility workers make proper decisions regarding generation scheduling, size of spinning reserve, and maintaining equilibrium between the power generated by the utility to fulfil the load demand. However, the development of an effective VSTLF model is challenging in gathering noisy real-time data and complicates features found in load demand variations from time to time. A hybrid approach for VSTLF using an incomplete fuzzy decision system (IFDS) combined with a genetic algorithm (GA) based feature selection technique for load forecasting in an hour ahead format is proposed in this research work. This proposed work aims to determine the load features and eliminate redundant features to form a less complex forecasting model. The proposed method considers the time of the day, temperature, humidity, and dew point as inputs and generates output as forecasted load. The input data and historical load data are collected from the Northern Regional Load Dispatch Centre (NRLDC) New Delhi for December 2009, January 2010 and February 2010. For validation of proposed method efficacy, it’s performance is further compared with other conventional AI techniques like ANN and ANFIS, which are integrated with genetic algorithm-based feature selection technique to boost their performance. These techniques’ accuracy is tested through their mean absolute percentage error (MAPE) and normalized root mean square error (nRMSE) value. Compared to other conventional AI techniques and other methods provided through previous studies, the proposed method is found to have acceptable accuracy for 1 h ahead of electrical load forecasting.

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