Open AccessThreshold benchmarking for feature ranking techniques
Author(s) -
Ruchika Malhotra,
Anjali Sharma
Publication year - 2021
Publication title -
bulletin of electrical engineering and informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.251
H-Index - 12
ISSN - 2302-9285
DOI - 10.11591/eei.v10i2.2752
Subject(s) - interpretability , ranking (information retrieval) , benchmarking , feature (linguistics) , benchmark (surveying) , range (aeronautics) , artificial intelligence , threshold limit value , computer science , pattern recognition (psychology) , identification (biology) , data mining , machine learning , variance (accounting) , learning to rank , mathematics , engineering , medicine , linguistics , philosophy , botany , environmental health , geodesy , accounting , marketing , aerospace engineering , business , biology , geography
In prediction modeling, the choice of features chosen from the original feature set is crucial for accuracy and model interpretability. Feature ranking techniques rank the features by its importance but there is no consensus on the number of features to be cut-off. Thus, it becomes important to identify a threshold value or range, so as to remove the redundant features. In this work, an empirical study is conducted for identification of the threshold benchmark for feature ranking algorithms. Experiments are conducted on Apache Click dataset with six popularly used ranker techniques and six machine learning techniques, to deduce a relationship between the total number of input features (N) to the threshold range. The area under the curve analysis shows that ≃ 33-50% of the features are necessary and sufficient to yield a reasonable performance measure, with a variance of 2%, in defect prediction models. Further, we also find that the log2(N) as the ranker threshold value represents the lower limit of the range.