scholarly journals Application of Metamorphic Testing to Supervised Classifiers

2009 ◽  
Author(s):  
Xiaoyuan Xie ◽  
Joshua Ho ◽  
Christian Murphy ◽  
Gail Kaiser ◽  
Baowen Xu ◽  
...  
Keyword(s):  
Metamorphic Testing ◽  
Supervised Classifiers

Research on metamorphic testing of slope and aspect calculating programs

2013 ◽  
Vol 33 (6) ◽  
pp. 1657-1661 ◽  
Author(s):  
SONG HUANG ◽  
Ruihao DING ◽  
Hui LI ◽  
Yi YAO
Keyword(s):  
Metamorphic Testing

scholarly journals High-wearable EEG-based distraction detection in motor rehabilitation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Andrea Apicella ◽  
Pasquale Arpaia ◽  
Mirco Frosolone ◽  
Nicola Moccaldi
Keyword(s):  
Automated System ◽  
Pass Band ◽  
Motor Rehabilitation ◽  
Common Spatial Pattern ◽  
K Nearest Neighbors ◽  
Dry Electrodes ◽  
Wearable Eeg ◽  
Band Filter ◽  
Therapy Effectiveness ◽  
Supervised Classifiers

AbstractA method for EEG-based distraction detection during motor-rehabilitation tasks is proposed. A wireless cap guarantees very high wearability with dry electrodes and a low number of channels. Experimental validation is performed on a dataset from 17 volunteers. Different feature extractions from spatial, temporal, and frequency domain and classification strategies were evaluated. The performances of five supervised classifiers in discriminating between attention on pure movement and with distractors were compared. A k-Nearest Neighbors classifier achieved an accuracy of 92.8 ± 1.6%. In this last case, the feature extraction is based on a custom 12 pass-band Filter-Bank (FB) and the Common Spatial Pattern (CSP) algorithm. In particular, the mean Recall of classification (percentage of true positive in distraction detection) is higher than 92% and allows the therapist or an automated system to know when to stimulate the patient’s attention for enhancing the therapy effectiveness.

Experience

2021 ◽  
Vol 13 (1) ◽  
pp. 1-16
Author(s):  
Michela Fazzolari ◽  
Francesco Buccafurri ◽  
Gianluca Lax ◽  
Marinella Petrocchi
Keyword(s):  
Relative Frequency ◽  
Service Providers ◽  
Real Data ◽  
Online Reviews ◽  
Purchase Decision ◽  
The Past ◽  
Supervised Classifiers ◽  
Relative Frequency Distribution ◽  
Opinion Spam ◽  
Fake Reviews

Over the past few years, online reviews have become very important, since they can influence the purchase decision of consumers and the reputation of businesses. Therefore, the practice of writing fake reviews can have severe consequences on customers and service providers. Various approaches have been proposed for detecting opinion spam in online reviews, especially based on supervised classifiers. In this contribution, we start from a set of effective features used for classifying opinion spam and we re-engineered them by considering the Cumulative Relative Frequency Distribution of each feature. By an experimental evaluation carried out on real data from Yelp.com, we show that the use of the distributional features is able to improve the performances of classifiers.

Stability evaluation for text localization systems via metamorphic testing

2021 ◽  
pp. 111040
Author(s):  
Rongjie Yan ◽  
Siqi Wang ◽  
Yixuan Yan ◽  
Hongyu Gao ◽  
Jun Yan
Keyword(s):  
Stability Evaluation ◽  
Text Localization ◽  
Metamorphic Testing ◽  
Localization Systems
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