Recommendation System Based on Optimal Feature Selection Algorithm for Predictive Analysis

2020 ◽  
pp. 105-119
Author(s):  
Malaichamy Vithya ◽  
Suguna Sangaiah
Keyword(s):  
Feature Selection ◽  
Recommendation System ◽  
Predictive Analysis ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Optimal Feature Selection ◽  
Optimal Feature

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.

scholarly journals Adaptive Salp Swarm Algorithm as Optimal Feature Selection for Power Quality Disturbance Classification

2021 ◽  
Vol 11 (12) ◽  
pp. 5670
Author(s):  
Supanat Chamchuen ◽  
Apirat Siritaratiwat ◽  
Pradit Fuangfoo ◽  
Puripong Suthisopapan ◽  
Pirat Khunkitti
Keyword(s):  
Feature Selection ◽  
Power Quality ◽  
Classification System ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Salp Swarm Algorithm ◽  
Power Quality Disturbance ◽  
Optimal Feature Selection ◽  
Swarm Algorithm ◽  
Optimal Feature

Power quality disturbance (PQD) is an influential situation that significantly declines the reliability of electrical distribution systems. Therefore, PQD classification is an important process for preventing system reliability degradation. This paper introduces a novel algorithm called “adaptive salp swarm algorithm (SSA)” as an optimal feature selection algorithm for PQD classification. Feature extraction and classifier of the proposed classification system were based on the discrete wavelet and the probabilistic neural network, respectively. The classification was focused on the 13 types of power quality signals. The optimal number of selected features for the proposed classification system was firstly determined. Then, it demonstrated that the optimally selected features resulted in the highest classification accuracy of 98.77%. High performance of the proposed classification system in the noisy environment, as well as based on the real dataset was also verified. Furthermore, the proposed SSA indicates a very high convergence rate compared to other well-known algorithms. A comparison of the proposed classification system’s performance to existing works was also carried out, revealing that the proposed system’s accuracy is on a high-range scale. Hence, the adaptive SSA becomes another efficient optimal feature selection algorithm for PQD classification.

Modulation Recognition of Digital Multimedia Signal Based on Data Feature Selection

2017 ◽  
Vol 8 (3) ◽  
pp. 90-111 ◽  
Author(s):  
Hui Wang ◽  
Li Li Guo ◽  
Yun Lin
Keyword(s):  
Feature Selection ◽  
Information Entropy ◽  
Feature Subset ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Modulation Recognition ◽  
Signal Modulation ◽  
Digital Multimedia ◽  
Optimal Feature Subset ◽  
Optimal Feature

Automatic modulation recognition is very important for the receiver design in the broadband multimedia communication system, and the reasonable signal feature extraction and selection algorithm is the key technology of Digital multimedia signal recognition. In this paper, the information entropy is used to extract the single feature, which are power spectrum entropy, wavelet energy spectrum entropy, singular spectrum entropy and Renyi entropy. And then, the feature selection algorithm of distance measurement and Sequential Feature Selection(SFS) are presented to select the optimal feature subset. Finally, the BP neural network is used to classify the signal modulation. The simulation result shows that the four-different information entropy can be used to classify different signal modulation, and the feature selection algorithm is successfully used to choose the optimal feature subset and get the best performance.

User-Level Ultra-Short-Term Load Forecasting Model Based on Optimal Feature Selection and Bahdanau Attention Mechanism

2021 ◽  
pp. 2150279
Author(s):  
Ziyao Wang ◽  
Huaqiang Li ◽  
Zizhuo Tang ◽  
Yang Liu
Keyword(s):  
Feature Selection ◽  
Power Consumption ◽  
Electric Power ◽  
Load Forecasting ◽  
Forecasting Model ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Short Term ◽  
Short Term Load Forecasting ◽  
Optimal Feature

Accurate ultra-short-term load forecasting is of great significance for real-time power generation scheduling and development of power cyber physical systems (Power CPS). However, in order to forecast the future load using the current high-dimensional, diverse and heterogeneous electric power consumption information, new challenges have been raised to the effective feature selection and the accurate load forecasting algorithms. However, very limited existing works consider the feature selection for the electric power consumption information and impacts to the thereafter load forecasting model. In view of this point, features that are critical to the load forecasting are selected using an embedded feature selection algorithm based on LightGBM to form an optimal feature set, with which a sequence to sequence (S2S) and gated recurrent unit (GRU)-based ultra-short-term load forecasting model that incorporates Bahdanau attention (BA) mechanism is presented. The S2S-GRU model is based on an encoding–decoding framework that is compatible to the input and output data series with variable lengths. By introducing the BA mechanism, loss of previous information issue of GRU can be solved. Experimental results show that first the presented feature selection algorithm can help to improve the performance of the load forecasting model. Second, the presented load forecasting model can find a compromise between the forecasting efficiency and accuracy.

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