A Feature Selection Algorithm for Anomaly Detection in Grid Environment Using k-fold Cross Validation Technique

2016 ◽  
pp. 619-630
Author(s):  
Dahliyusmanto ◽  
Tutut Herawan ◽  
Syefrida Yulina ◽  
Abdul Hanan Abdullah
Keyword(s):  
Feature Selection ◽  
Anomaly Detection ◽  
Cross Validation ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Grid Environment ◽  
Validation Technique ◽  
Fold Cross Validation

AN EFFICIENT FEATURE SELECTION ALGORITHM FOR COMPUTER-AIDED POLYP DETECTION

2006 ◽  
Vol 15 (06) ◽  
pp. 893-915 ◽  
Author(s):  
JIANG LI ◽  
JIANHUA YAO ◽  
RONALD M. SUMMERS ◽  
NICHOLAS PETRICK ◽  
MICHAEL T. MANRY ◽  
...  
Keyword(s):  
Feature Selection ◽  
Cross Validation ◽  
Search Algorithm ◽  
Piecewise Linear ◽  
Least Square ◽  
Support Vector ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Polyp Detection ◽  
Computer Aided

We present an efficient feature selection algorithm for computer aided detection (CAD) computed tomographic (CT) colonography. The algorithm (1) determines an appropriate piecewise linear network (PLN) model by cross validation, (2) applies the orthonormal least square (OLS) procedure to the PLN model utilizing a Modified Schmidt procedure, and (3) uses a floating search algorithm to select features that minimize the output variance. The undesirable "nesting effect" is prevented by the floating search approach, and the piecewise linear OLS procedure makes this algorithm very computationally efficient because the Modified Schmidt procedure only requires one data pass during the whole searching process. The selected features are compared to those obtained by other methods, through cross validation with support vector machines (SVMs).

scholarly journals High-Accuracy Power Quality Disturbance Classification Using the Adaptive ABC-PSO as Optimal Feature Selection Algorithm

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1238
Author(s):  
Supanat Chamchuen ◽  
Apirat Siritaratiwat ◽  
Pradit Fuangfoo ◽  
Puripong Suthisopapan ◽  
Pirat Khunkitti
Keyword(s):  
Feature Selection ◽  
Power Quality ◽  
Distribution System ◽  
Classification Accuracy ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Electrical Distribution ◽  
Power Quality Disturbance ◽  
Optimal Feature Selection ◽  
Optimal Feature

Power quality disturbance (PQD) is an important issue in electrical distribution systems that needs to be detected promptly and identified to prevent the degradation of system reliability. This work proposes a PQD classification using a novel algorithm, comprised of the artificial bee colony (ABC) and the particle swarm optimization (PSO) algorithms, called “adaptive ABC-PSO” as the feature selection algorithm. The proposed adaptive technique is applied to a combination of ABC and PSO algorithms, and then used as the feature selection algorithm. A discrete wavelet transform is used as the feature extraction method, and a probabilistic neural network is used as the classifier. We found that the highest classification accuracy (99.31%) could be achieved through nine optimally selected features out of all 72 extracted features. Moreover, the proposed PQD classification system demonstrated high performance in a noisy environment, as well as the real distribution system. When comparing the presented PQD classification system’s performance to previous studies, PQD classification accuracy using adaptive ABC-PSO as the optimal feature selection algorithm is considered to be at a high-range scale; therefore, the adaptive ABC-PSO algorithm can be used to classify the PQD in a practical electrical distribution system.

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