Unsupervised feature selection technique based on harmony search algorithm for improving the text clustering

2016 ◽  
Author(s):  
Laith Mohammad Abualigah ◽  
Ahamad Tajudin Khader ◽  
Mohammed Azmi Al-Betar
Keyword(s):  
Feature Selection ◽  
Search Algorithm ◽  
Harmony Search ◽  
Text Clustering ◽  
Harmony Search Algorithm ◽  
Feature Selection Technique ◽  
Unsupervised Feature Selection ◽  
Selection Technique

scholarly journals Recent Development in Electricity Price Forecasting Based on Computational Intelligence Techniques in Deregulated Power Market

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6104
Author(s):  
Alireza Pourdaryaei ◽  
Mohammad Mohammadi ◽  
Mazaher Karimi ◽  
Hazlie Mokhlis ◽  
Hazlee A. Illias ◽  
...  
Keyword(s):  
Feature Selection ◽  
Search Algorithm ◽  
Power Market ◽  
Electricity Price ◽  
Price Forecasting ◽  
Feature Selection Technique ◽  
Selection Technique ◽  
Backtracking Search ◽  
Electricity Price Forecasting ◽  
Input Variables

The development of artificial intelligence (AI) based techniques for electricity price forecasting (EPF) provides essential information to electricity market participants and managers because of its greater handling capability of complex input and output relationships. Therefore, this research investigates and analyzes the performance of different optimization methods in the training phase of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) for the accuracy enhancement of EPF. In this work, a multi-objective optimization-based feature selection technique with the capability of eliminating non-linear and interacting features is implemented to create an efficient day-ahead price forecasting. In the beginning, the multi-objective binary backtracking search algorithm (MOBBSA)-based feature selection technique is used to examine various combinations of input variables to choose the suitable feature subsets, which minimizes, simultaneously, both the number of features and the estimation error. In the later phase, the selected features are transferred into the machine learning-based techniques to map the input variables to the output in order to forecast the electricity price. Furthermore, to increase the forecasting accuracy, a backtracking search algorithm (BSA) is applied as an efficient evolutionary search algorithm in the learning procedure of the ANFIS approach. The performance of the forecasting methods for the Queensland power market in the year 2018, which is well-known as the most competitive market in the world, is investigated and compared to show the superiority of the proposed methods over other selected methods.

Hybrid Harmony Search Algorithm to Solve the Feature Selection for Data Mining Applications

2020 ◽  
pp. 19-37 ◽  
Author(s):  
Laith Mohammad Abualigah ◽  
Mofleh Al‐diabat ◽  
Mohammad Al Shinwan ◽  
Khaldoon Dhou ◽  
Bisan Alsalibi ◽  
...  
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Selection For

scholarly journals Feature Selection for Facial Emotion Recognition Using Cosine Similarity-Based Harmony Search Algorithm

2020 ◽  
Vol 10 (8) ◽  
pp. 2816 ◽  
Author(s):  
Soumyajit Saha ◽  
Manosij Ghosh ◽  
Soulib Ghosh ◽  
Shibaprasad Sen ◽  
Pawan Kumar Singh ◽  
...  
Keyword(s):  
Feature Selection ◽  
Facial Expression ◽  
Emotion Recognition ◽  
Local Binary Pattern ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Cosine Similarity ◽  
Facial Emotion Recognition ◽  
Facial Emotion

Nowadays, researchers aim to enhance man-to-machine interactions by making advancements in several domains. Facial emotion recognition (FER) is one such domain in which researchers have made significant progresses. Features for FER can be extracted using several popular methods. However, there may be some redundant/irrelevant features in feature sets. In order to remove those redundant/irrelevant features that do not have any significant impact on classification process, we propose a feature selection (FS) technique called the supervised filter harmony search algorithm (SFHSA) based on cosine similarity and minimal-redundancy maximal-relevance (mRMR). Cosine similarity aims to remove similar features from feature vectors, whereas mRMR was used to determine the feasibility of the optimal feature subsets using Pearson’s correlation coefficient (PCC), which favors the features that have lower correlation values with other features—as well as higher correlation values with the facial expression classes. The algorithm was evaluated on two benchmark FER datasets, namely the Radboud faces database (RaFD) and the Japanese female facial expression (JAFFE). Five different state-of-the-art feature descriptors including uniform local binary pattern (uLBP), horizontal–vertical neighborhood local binary pattern (hvnLBP), Gabor filters, histogram of oriented gradients (HOG) and pyramidal HOG (PHOG) were considered for FS. Obtained results signify that our technique effectively optimized the feature vectors and made notable improvements in overall classification accuracy.

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