scholarly journals Compare failure prediction models based on feature selection technique: empirical case from Iran

2011 ◽  
Vol 3 ◽  
pp. 568-573 ◽  
Author(s):  
Sarah Ashoori ◽  
Shahriar Mohammadi
Keyword(s):  
Feature Selection ◽  
Prediction Models ◽  
Failure Prediction ◽  
Feature Selection Technique ◽  
Selection Technique

A STUDY OF SOFTWARE METRIC SELECTION TECHNIQUES: STABILITY ANALYSIS AND DEFECT PREDICTION MODEL PERFORMANCE

2013 ◽  
Vol 22 (05) ◽  
pp. 1360010 ◽  
Author(s):  
HUANJING WANG ◽  
TAGHI M. KHOSHGOFTAAR ◽  
QIANHUI (ALTHEA) LIANG
Keyword(s):  
Feature Selection ◽  
Prediction Model ◽  
Prediction Models ◽  
Model Performance ◽  
Classification Model ◽  
Defect Prediction ◽  
Feature Selection Technique ◽  
Selection Technique ◽  
Metric Selection ◽  
Feature Selection Techniques

Software metrics (features or attributes) are collected during the software development cycle. Metric selection is one of the most important preprocessing steps in the process of building defect prediction models and may improve the final prediction result. However, the addition or removal of program modules (instances or samples) can alter the subsets chosen by a feature selection technique, rendering the previously-selected feature sets invalid. Very limited research have been done considering both stability (or robustness) and defect prediction model performance together in the software engineering domain, despite the importance of both aspects when choosing a feature selection technique. In this paper, we test the stability and classification model performance of eighteen feature selection techniques as the magnitude of change to the datasets and the size of the selected feature subsets are varied. All experiments were conducted on sixteen datasets from three real-world software projects. The experimental results demonstrate that Gain Ratio shows the least stability while two different versions of ReliefF show the most stability, followed by the PRC- and AUC-based threshold-based feature selection techniques. Results also show that the signal-to-noise ranker performed moderately in terms of robustness and was the best ranker in terms of model performance. Finally, we conclude that while for some rankers, stability and classification performance are correlated, this is not true for other rankers, and therefore performance according to one scheme (stability or model performance) cannot be used to predict performance according to the other.

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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