scholarly journals Social spider optimisation algorithm for dimension reduction of electroencephalogram signals in human emotion recognition

2018 ◽  
Vol 7 (2.15) ◽  
pp. 146
Author(s):  
Abdullah Yousef Al-Qammaz ◽  
Farzana Kabir Ahmad ◽  
Yuhanis Yusof
Keyword(s):  
Feature Selection ◽  
Emotion Recognition ◽  
Heuristic Algorithms ◽  
Feature Selection Method ◽  
Classification Performance ◽  
Selection Method ◽  
Social Spider ◽  
Comparable Performance ◽  
Searching Method ◽  
Optimal Feature

Due to some limitations of current heuristics and evolutionary algorithms, this paper proposed a new swarm based algorithm for feature selection method called Social Spider Optimization (SSO-FS). In this research, SSO-FS is used in the EEG-based emotion recognition model as searching method to find optimal feature set to maximize classification performance and mimics the cooperative behaviour and mechanism of social spiders in nature. This proposed feature selection method has been tested on DEAP EEG dataset with six subjects and compared with the most popular heuristic algorithms such as GA, PSO and ABC. The results show that the SSO-FS provides a remarkable and comparable performance compared to other existing methods. Whereby, the max accuracy obtained is 66.66% and 70.83%, the mean accuracy obtained is 55.51 7.17 and 60.97 8.38 for 3-level of valence emotions and 3-level of arousal emotions classification respectively.  

scholarly journals Deep Recurrent Network Based Feature Selection using Single Matrix Normalization and Eigen Vectors for Analyzing Sentiments

2019 ◽  
Vol 8 (10) ◽  
pp. 804-809
Keyword(s):  
Social Media ◽  
Feature Selection ◽  
Feature Selection Method ◽  
Recurrent Network ◽  
Classification Performance ◽  
Selection Method ◽  
Data Set ◽  
Improve Accuracy ◽  
N Gram ◽  
Single Matrix

Sentiment analysis plays a major role in e-commerce and social media these days. Due to the increasing growth of social media, a huge number of peoples and users send their reviews through the Internet and several other sources. Analyzing this data is challenging in today's life. In this paper new normalization based feature selection method is proposed and the topic of interest here is to select the relevant features and perform the classification of the data and find the accuracy. Stability of the data is considered as the most important challenge in analyzing the sentiments. In this paper investigating the sentiments and selecting the relevant features from the data set places a major role. The aim is to work with the vector-based feature selection and check the classification performance using recurrent networks. In this paper, text mining depends on feature retrieval methods to improve accuracy and propose a single matrix normalization method to reduce the dimensions. The proposed method performs data preprocessing or sentiment classification and features reduction to improve accuracy. The proposed method achieves better accuracy than the N-gram feature selection method. The experimental results show that the proposed method has better accuracy than other traditional feature selection approaches and that the proposed method can decrease the implementation time.

scholarly journals Missing Value Aware Optimal Feature Selection Method for Efficient Big Data Mining Process

2019 ◽  
Vol 8 (2S11) ◽  
pp. 354-360 ◽  
Keyword(s):  
Feature Selection ◽  
Research Method ◽  
Missing Values ◽  
Research Work ◽  
Feature Selection Method ◽  
Selection Method ◽  
Missing Value ◽  
Optimal Feature Selection ◽  
Enhanced Particle Swarm Optimization ◽  
Optimal Feature

Big mining plays a more critical role in the real world environment due to presence of large volume of data with different varieties and type. Handling these data values and predicting the information would be the more difficult task which needs to be concerned more to obtain the useful knowledge. This is achieved in our previous research work by introducing the Enhanced Particle Swarm Optimization with Genetic Algorithm – Modified Artificial Neural Network (EPSOGA -MANN) which can select the optimal features from the big volume of data. However this research work might be reduced in its performance due to presence of missing values in the dataset. And also this method is more complex to perform due to increased computational overhead of ANN algorithm. This is resolved in the proposed research method by introducing the method namely Missing Value concerned Optimal Feature Selection Method (MV-OFSM). In this research method Improved KNN imputation algorithm is introduced to handle the missing values. And then Dynamic clustering method is introduced to cluster the dataset based on closeness measure. Then Anarchies Society Optimization (ASO) based feature selection approach is applied for performing feature selection in the given dataset. Finally a Hybrid ANN-GA classification technique is applied for implementing the classification. The overall performance evaluation of the research method is performed in the matlab simulation environment from which it is proved that the proposed research method leads to provide the better performance than the existing research technique.

Artificial Bee Colony–Based Feature Selection Algorithm for Cyberbullying

2020 ◽  
Author(s):  
Esra Sarac Essiz ◽  
Murat Oturakci
Keyword(s):  
Feature Selection ◽  
Artificial Bee Colony ◽  
Feature Selection Method ◽  
Classification Performance ◽  
Selection Method ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Traditional Methods ◽  
Bee Colony ◽  
Nature Inspired Algorithm

Abstract As a nature-inspired algorithm, artificial bee colony (ABC) is an optimization algorithm that is inspired by the search behaviour of honey bees. The main aim of this study is to examine the effects of the ABC-based feature selection algorithm on classification performance for cyberbullying, which has become a significant worldwide social issue in recent years. With this purpose, the classification performance of the proposed ABC-based feature selection method is compared with three different traditional methods such as information gain, ReliefF and chi square. Experimental results present that ABC-based feature selection method outperforms than three traditional methods for the detection of cyberbullying. The Macro averaged F_measure of the data set is increased from 0.659 to 0.8 using proposed ABC-based feature selection method.

PERFORMANCE ANALYSIS OF FEATURE SELECTION METHOD USING ANOVA FOR AUTOMATIC WHEEZE DETECTION

2015 ◽  
Vol 77 (7) ◽  
Author(s):  
Syamimi Mardiah Shaharum ◽  
Kenneth Sundaraj ◽  
Khaled Helmy
Keyword(s):  
Feature Selection ◽  
Classification Accuracy ◽  
Feature Selection Method ◽  
Classification Performance ◽  
Selection Method ◽  
Performance Measure ◽  
High Dimensional ◽  
Artificial Neural Network Ann ◽  
Performance Rate ◽  
One Way Anova

In this work, we show that the classification performance of a high-dimensional features data can be improved by applying feature selection method. One-way ANOVA were utilized and to evaluate the performance measure of the feature selection method, Artificial Neural Network (ANN) was used. From the results obtained, it can be concluded that ANN performance using feature that undergo feature selection method produce a better classification accuracy compared to the ANN performance using feature that did not undergo feature selection method with 93.33% against 80.00% accuracy achieved. Therefore can be conclude that feature selection is a process that is crucial to be done in order to produce a good performance rate. 

scholarly journals A Hybrid Meta-Heuristic Feature Selection Method Using Golden Ratio and Equilibrium Optimization Algorithms for Speech Emotion Recognition

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 200953-200970
Author(s):  
Arijit Dey ◽  
Soham Chattopadhyay ◽  
Pawan Kumar Singh ◽  
Ali Ahmadian ◽  
Massimiliano Ferrara ◽  
...  
Keyword(s):  
Feature Selection ◽  
Emotion Recognition ◽  
Golden Ratio ◽  
Optimization Algorithms ◽  
Feature Selection Method ◽  
Selection Method ◽  
Speech Emotion Recognition
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