Search Results - (( data selection method algorithm ) OR ( features extraction method algorithm ))
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Evaluation and Comparative Analysis of Feature Extraction Methods on Image Data to increase the Accuracy of Classification Algorithms
Published 2024“…The feature extraction methods evaluated were Grayscale Pixel Values, Mean Pixel Value of Channels, and Extracting Edge Features. …”
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2
Genetic algorithm based ensemble framework for sentiment analysis
Published 2018“…Prior to developing the whole multilayered ensemble framework, two separate experiments were performed to evaluate and study the different methods of feature extraction and selection. Methods of feature extraction can be separated into word based and phrase based. …”
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Thesis -
3
Utilizing artificial bee colony algorithm as feature selection method in Arabic text classification
Published 2023“…One of the widely used algorithms for feature selection in text classification is the Evolutionary algorithm . …”
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Performance comparison of feature selection methods for prediction in medical data
Published 2023“…This study analyzes filter, wrapper, and embedded feature selection methods for medical data with the predictive machine learn- ing algorithm, Random Forest and CatBoost. …”
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Proceeding Paper -
5
Electroencephalogram-based decoding cognitive states using convolutional neural network and likelihood ratio based score fusion
Published 2017“…The wavelet transform-support vector machine method is the most popular currently used feature extraction and prediction method. …”
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The significant effect of feature selection methods in spam risk assessment using dendritic cell algorithm
Published 2024Subjects:conference output::conference proceedings::conference paper -
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Opposition Based Competitive Grey Wolf Optimizer For EMG Feature Selection
Published 2020“…Four state-of-the-art algorithms include particle swarm optimization, flower pollination algorithm, butterfly optimization algorithm, and CBGWO are used to examine the effectiveness of proposed methods in feature selection. …”
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A guided hybrid k-means and genetic algorithm models for children handwriting legibility performance assessment / Norzehan Sakamat
Published 2021“…Euclidean Distance, Pearson Correlation and Matching Matrix were used to measure the performance of the feature extraction and clustering methods. Recognition software achieved 87.14%, EPD algorithm achieved 73.57% and HMT algorithm achieved 74.30%) prediction accuracy with OTs. …”
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Edge detection and contour segmentation for fruit classification in natural environment / Khairul Adilah Ahmad
Published 2018“…Feature extraction and selection reduces the number of features. …”
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10
A novel framework for identifying twitter spam data using machine learning algorithms
Published 2020“…The feature selection is introduced to select the most important from the extracted high-dimensional best features and feed the selected features into the minimum Redundancy and Maximal Relevance algorithm and apply random forest for classification. …”
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Temporal video segmentation using squared form of Krawtchouk-Tchebichef moments
Published 2018“…In the first part of the proposed algorithm, unique moments coefficients (features) are extracted using a new hybrid set of orthogonal polynomials which is derived based on the modified forms of Krawtchouk and a Tchebichef polynomials. …”
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12
Supervised ANN classification for engineering machined textures based on enhanced features extraction and reduction scheme
Published 2013“…The proposed methodology focuses mainly on three main stages for an input image, firstly extracting features by commonly used features extraction methods such as edge detection, and histogram. …”
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Conference or Workshop Item -
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Long-term electrical energy consumption: Formulating and forecasting via optimized gene expression programming / Seyed Hamidreza Aghay Kaboli
Published 2018“…In the developed feature selection approach, multi-objective binary-valued backtracking search algorithm (MOBBSA) is used as an efficient evolutionary search algorithm to search within different combinations of input variables and selects the non-dominated feature subsets, which minimize simultaneously both the estimation error and the number of features. …”
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14
The classification of motor imagery response: an accuracy enhancement through the ensemble of random subspace k-NN
Published 2021“…The performance of BCIs primarily depends on the efficacy of the feature extraction and feature selection techniques, as well as the classification algorithms employed. …”
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15
Design Of Feature Selection Methods For Hand Movement Classification Based On Electromyography Signals
Published 2020“…For this purpose, the feature selection (FS) method is applied to evaluate the best feature subset from a large available feature set. …”
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16
EEG Eye State Identification based on Statistical Feature and Common Spatial Pattern Filter
Published 2019“…The process of developing the EEG eye state classification algorithm, includes data extraction, pre-processing, data normalization, feature extraction, feature selection and classification are detailed out in this paper. …”
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Conference or Workshop Item -
17
EEG Eye State Identification based on Statistical Feature and Common Spatial Pattern Filter
Published 2019“…The process of developing the EEG eye state classification algorithm, includes data extraction, pre-processing, data normalization, feature extraction, feature selection and classification are detailed out in this paper. …”
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A COLLABORATIVE FRAMEWORK FOR ANDROID MALWARE IDENTIFICATION USING DYNAMIC ANALYSIS
Published 2019“…The scope of this project focuses on Android Malware detection by using dynamic analysis. The methods to implement this project is through data collection, feature extraction, feature selection, and classification process. …”
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Final Year Project Report / IMRAD -
20
Computational Technique for an Efficient Classification of Protein Sequences With Distance-Based Sequence Encoding Algorithm
Published 2017“…A statistical metric-based feature selection algorithm is then adopted to identify the reduced set of features to represent the original feature space. …”
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