Feature Selection with Particle Swarm Optimization for Human Activity Recognition Using Learning Vector Quantization

2021 ◽  
Author(s):  
Fitra A. Bachtiar ◽  
Zainal Arifien ◽  
Adzanil Rachmadhi Putra ◽  
Pasca Imanuddin Akbar
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Activity Recognition ◽  
Human Activity ◽  
Vector Quantization ◽  
Particle Swarm ◽  
Human Activity Recognition ◽  
Learning Vector Quantization ◽  
Swarm Optimization

scholarly journals A correlation-based binary particle swarm optimization method for feature selection in human activity recognition

2018 ◽  
Vol 14 (4) ◽  
pp. 155014771877278 ◽  
Author(s):  
Huaijun Wang ◽  
Ruomeng Ke ◽  
Junhuai Li ◽  
Yang An ◽  
Kan Wang ◽  
...  
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Activity Recognition ◽  
Human Activity ◽  
Nearest Neighbor ◽  
Particle Swarm ◽  
Optimization Method ◽  
Binary Particle Swarm Optimization ◽  
K Nearest Neighbor ◽  
Swarm Optimization

Effective feature selection determines the efficiency and accuracy of a learning process, which is essential in human activity recognition. In existing works, for simplification purposes, feature selection algorithms are mostly based on the assumption of feature independence. However, in some scenarios, the optimization method based on this independence hypothesis results in poor recognition performance. This article proposes a correlation-based binary particle swarm optimization method for feature selection in human activity recognition. In the proposed algorithm, the particle swarm optimization algorithm is no longer used as a black box. Meanwhile, correlation coefficients among the features are added to binary particle swarm optimization as a feature correlation factor to determine the position of particles, so that the feature with more information is more likely to be selected. The k-nearest neighbor classifier is then used as the fitness function in the particle swarm optimization to evaluate the performance of the feature subset, that is, feature combination with the highest k-nearest neighbor classifier recognition rate would be picked as the eigenvector. Experimental results show that the proposed method can work well with six classifiers, namely, J48, random forest, k-nearest neighbor, multilayer perceptron, naïve Bayesian, and support vector machine, and the new algorithm can improve the classification accuracy in the OPPORTUNITY Activity Recognition dataset.

scholarly journals 4W1H and Particle Swarm Optimization for Human Activity Recognition

2011 ◽  
Vol 15 (7) ◽  
pp. 793-799 ◽  
Author(s):  
Leon Palafox ◽  
◽  
Hideki Hashimoto
Keyword(s):  
Particle Swarm Optimization ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Activities ◽  
Particle Swarm ◽  
Human Activity Recognition ◽  
Self Organizing Maps ◽  
Swarm Optimization ◽  
Self Organizing

This paper proposes a paradigm in the forensic area for detecting and categorizing human activities. The presented approach uses five base variables, referred to as 4W1H (“Who,” “When,” “What,” “Where,” and “How”) to describe the context in an environment. The proposed system usesself-organizing mapsto classify movements for the “How” variable of 4W1H, as well asparticle swarm optimization clusteringtechniques for the grouping (clustering) of data obtained from observations. The paper describes the hardware settings required for detecting these variables and the system designed to do the sensing.

An Improved Intelligent Approach to Enhance the Sentiment Classifier for Knowledge Discovery Using Machine Learning

2020 ◽  
Vol 10 (4) ◽  
pp. 582-593
Author(s):  
Midde Venkateswarlu Naik ◽  
D. Vasumathi ◽  
A.P. Siva Kumar
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Global Warming ◽  
Particle Swarm Optimization ◽  
Sentiment Analysis ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Sentiment Classification ◽  
Support Vector ◽  
Swarm Optimization

Aims: The proposed research work is on an evolutionary enhanced method for sentiment or emotion classification on unstructured review text in the big data field. The sentiment analysis plays a vital role for current generation of people for extracting valid decision points about any aspect such as movie ratings, education institute or politics ratings, etc. The proposed hybrid approach combined the optimal feature selection using Particle Swarm Optimization (PSO) and sentiment classification through Support Vector Machine (SVM). The current approach performance is evaluated with statistical measures, such as precision, recall, sensitivity, specificity, and was compared with the existing approaches. The earlier authors have achieved an accuracy of sentiment classifier in the English text up to 94% as of now. In the proposed scheme, an average accuracy of sentiment classifier on distinguishing datasets outperformed as 99% by tuning various parameters of SVM, such as constant c value and kernel gamma value in association with PSO optimization technique. The proposed method utilized three datasets, such as airline sentiment data, weather, and global warming datasets, that are publically available. The current experiment produced results that are trained and tested based on 10- Fold Cross-Validations (FCV) and confusion matrix for predicting sentiment classifier accuracy. Background: The sentiment analysis plays a vital role for current generation people for extracting valid decisions about any aspect such as movie rating, education institute or even politics ratings, etc. Sentiment Analysis (SA) or opinion mining has become fascinated scientifically as a research domain for the present environment. The key area is sentiment classification on semi-structured or unstructured data in distinguish languages, which has become a major research aspect. User-Generated Content [UGC] from distinguishing sources has been hiked significantly with rapid growth in a web environment. The huge user-generated data over social media provides substantial value for discovering hidden knowledge or correlations, patterns, and trends or sentiment extraction about any specific entity. SA is a computational analysis to determine the actual opinion of an entity which is expressed in terms of text. SA is also called as computation of emotional polarity expressed over social media as natural text in miscellaneous languages. Usually, the automatic superlative sentiment classifier model depends on feature selection and classification algorithms. Methods: The proposed work used Support vector machine as classification technique and particle swarm optimization technique as feature selection purpose. In this methodology, we tune various permutations and combination parameters in order to obtain expected desired results with kernel and without kernel technique for sentiment classification on three datasets, including airline, global warming, weather sentiment datasets, that are freely hosted for research practices. Results: In the proposed scheme, The proposed method has outperformed with 99.2% of average accuracy to classify the sentiment on different datasets, among other machine learning techniques. The attained high accuracy in classifying sentiment or opinion about review text proves superior effectiveness over existing sentiment classifiers. The current experiment produced results that are trained and tested based on 10- Fold Cross-Validations (FCV) and confusion matrix for predicting sentiment classifier accuracy. Conclusion: The objective of the research issue sentiment classifier accuracy has been hiked with the help of Kernel-based Support Vector Machine (SVM) based on parameter optimization. The optimal feature selection to classify sentiment or opinion towards review documents has been determined with the help of a particle swarm optimization approach. The proposed method utilized three datasets to simulate the results, such as airline sentiment data, weather sentiment data, and global warming data that are freely available datasets.

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