scholarly journals Sentiment Classification using Neural Network and Ensemble Model based on Genetic Algorithm

2020 ◽  
Vol 9 (3) ◽  
pp. 1885-1891
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Feature Selection ◽  
Web Sites ◽  
Information Gain ◽  
Back Propagation ◽  
Feature Selection Method ◽  
Propagation Model ◽  
Feature Selection Technique ◽  
Fast Development

The fast development of web sites and the number of product on these websites are available. The purpose of classification of sentiment is to efficiently identify opinion expressed in text. This paper compares three different optimized models including genetic optimized feature selection method, Genetic Algorithm (GA), ensemble approach that uses information gain and genetic algorithm as feature selection methods incorporated SVM model, Genetic Bagging (GB) and the next method uses optimized feature selection as feature selection technique incorporated back propagation model, Genetic Neural Network (GNN) models are compared. We are tested in sentiment analysis using sample multi-domain review datasets and movie review dataset.. These approaches are tested using various quality metrics and the results show that the Genetic Bagging (GB) technique outperforms in classifying the sentiment of the multi domain reviews and movie reviews. An empirical analysis is performed to compare the level of importance of the classifiers GB, GNN methods with McNemar’s statistical method.

scholarly journals Thai Water Buffalo Disease Analysis with the Application of Feature Selection Technique and Multi-Layer Perceptron Neural Network

2021 ◽  
Vol 11 (2) ◽  
pp. 6907-6911
Author(s):  
S. Nuanmeesri ◽  
W. Sriurai
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Water Buffalo ◽  
Information Gain ◽  
Sampling Technique ◽  
Multi Layer Perceptron ◽  
Analysis Model ◽  
Feature Selection Technique ◽  
Selection Technique ◽  
Correlation Based Feature Selection

This research aims to develop the analysis model for diseases in water buffalo towards the application of the feature selection technique along with the Multi-Layer Perceptron neural network. The data used for analysis was collected from books and documents related to diseases in water buffalo and the official website of the Department of Livestock Development. The data consists of the characteristics of six diseases in water buffalo, including Anthrax disease, Hemorrhagic Septicemia, Brucellosis, Foot and Mouth disease, Parasitic disease, and Mastitis. Since the amount of the collected data was limited, the Synthetic Minority Over-sampling Technique was also employed to adjust the imbalance dataset. Afterward, the adjusted dataset was used to select the disease characteristics towards the application of two feature selection techniques, including Correlation-based Feature Selection and Information Gain. Subsequently, the selected features were then used for developing the analysis model for diseases in water buffalo towards the use of Multi-Layer Perceptron neural network. The evaluation results of the model’s effectiveness, given by the 10-fold cross-validation, showed that the analysis model for diseases in water buffalo developed by Correlation-based Feature Selection and Multi-Layer Perceptron neural network provided the highest level of effectiveness with the accuracy of 99.71%, the precision of 99.70%, and the recall of 99.72%. This implies that the analysis model is effectively applicable.

scholarly journals Effect on speech emotion classification of a feature selection approach using a convolutional neural network

2021 ◽  
Vol 7 ◽  
pp. e766
Author(s):  
Ammar Amjad ◽  
Lal Khan ◽  
Hsien-Tsung Chang
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Convolutional Neural Network ◽  
Feature Selection Method ◽  
Classification Problem ◽  
Speech Emotion Recognition ◽  
Support Vector ◽  
Emotion Classification ◽  
K Nearest Neighbors ◽  
Feature Selection Technique

Speech emotion recognition (SER) is a challenging issue because it is not clear which features are effective for classification. Emotionally related features are always extracted from speech signals for emotional classification. Handcrafted features are mainly used for emotional identification from audio signals. However, these features are not sufficient to correctly identify the emotional state of the speaker. The advantages of a deep convolutional neural network (DCNN) are investigated in the proposed work. A pretrained framework is used to extract the features from speech emotion databases. In this work, we adopt the feature selection (FS) approach to find the discriminative and most important features for SER. Many algorithms are used for the emotion classification problem. We use the random forest (RF), decision tree (DT), support vector machine (SVM), multilayer perceptron classifier (MLP), and k-nearest neighbors (KNN) to classify seven emotions. All experiments are performed by utilizing four different publicly accessible databases. Our method obtains accuracies of 92.02%, 88.77%, 93.61%, and 77.23% for Emo-DB, SAVEE, RAVDESS, and IEMOCAP, respectively, for speaker-dependent (SD) recognition with the feature selection method. Furthermore, compared to current handcrafted feature-based SER methods, the proposed method shows the best results for speaker-independent SER. For EMO-DB, all classifiers attain an accuracy of more than 80% with or without the feature selection technique.

scholarly journals Opinion classification on a social network by a novel feature selection technique

2020 ◽  
Vol 20 (2) ◽  
pp. 960
Author(s):  
Atchara Choompol ◽  
Panida Songram ◽  
Phattahanaphong Chomphuwiset
Keyword(s):  
Feature Selection ◽  
Social Network ◽  
Association Rules ◽  
Information Gain ◽  
Feature Selection Method ◽  
Tuning Parameter ◽  
Computational Time ◽  
Chi Square ◽  
Feature Selection Technique ◽  
Filter Model

Most of the opinion comments on social networks are short and ambiguous. In general, opinion classification on the comments is difficult because of lacking dominant features. A feature extraction technique is therefore necessary for improving accuracy of the classification and computational time. This paper proposes an effective feature selection method for opinion classification on a social network. The proposed method selects features based on the concept of a filter model, together with association rules. Support and confidence are used to calculate the weights of features. The features with high weight are selected for classification. Unlike supports in association rules, supports in our method are normalized to 0-1 to remove outlier supports. Moreover, a tuning parameter is used to emphasize the degree of support or confidence. The experimental results show that the proposed method provides high classification efficiency. The proposed method outperforms Information Gain, Chi-Square, and Gini Index in both computational time and accuracy.

Sentiment classification using hybrid feature selection and ensemble classifier

2021 ◽  
pp. 1-10 ◽  
Author(s):  
Achin Jain ◽  
Vanita Jain
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Information Gain ◽  
Statistical Significance ◽  
Sentiment Classification ◽  
Ensemble Classification ◽  
Support Vector ◽  
Svm Classifier ◽  
Feature Selection Technique ◽  
Restaurant Reviews

This paper presents a Hybrid Feature Selection Technique for Sentiment Classification. We have used a Genetic Algorithm and a combination of existing Feature Selection methods, namely: Information Gain (IG), CHI Square (CHI), and GINI Index (GINI). First, we have obtained features from three different selection approaches as mentioned above and then performed the UNION SET Operation to extract the reduced feature set. Then, Genetic Algorithm is applied to optimize the feature set further. This paper also presents an Ensemble Approach based on the error rate obtained different domain datasets. To test our proposed Hybrid Feature Selection and Ensemble Classification approach, we have considered four Support Vector Machine (SVM) classifier variants. We have used UCI ML Datasets of three domains namely: IMDB Movie Review, Amazon Product Review and Yelp Restaurant Reviews. The experimental results show that our proposed approach performed best in all three domain datasets. Further, we also presented T-Test for Statistical Significance between classifiers and comparison is also done based on Precision, Recall, F1-Score, AUC and model execution time.

scholarly journals Prediction of pilling of polyester–cotton blended woven fabric using artificial neural network models

2020 ◽  
Vol 15 ◽  
pp. 155892501990015 ◽  
Author(s):  
Qi Xiao ◽  
Rui Wang ◽  
Shujie Zhang ◽  
Danyang Li ◽  
Hongyu Sun ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Woven Fabrics ◽  
Propagation Model ◽  
Woven Fabric ◽  
Neural Network Models ◽  
Optimized Model ◽  
Artificial Neural

In this article, an intelligent pilling prediction model using back-propagation neural network model and an optimized model with genetic algorithm is introduced. Genetic algorithm is proposed in consideration of the initial weight and threshold of back-propagation artificial neural network, and further improves training speed and the accuracy for prediction pilling of polyester–cotton blended woven fabrics. The results show that the maximum numbers of training steps of the optimized model by genetic algorithm are reduced from 164 steps to 137 steps compared with that of back-propagation model. The training fitness of optimized model by genetic algorithm is improved from 0.914 to 0.945. The simulation fitness is increased from 0.912 to 0.987. And the root mean square error decreased from 1.0431 to 0.6842. The optimized model by genetic algorithm shows a better agreement between the experimental and predicted values.

SPEECH EMOTION RECOGNITION METHOD BASED ON IMPROVED DECISION TREE AND LAYERED FEATURE SELECTION

2010 ◽  
Vol 07 (02) ◽  
pp. 245-261 ◽  
Author(s):  
QIRONG MAO ◽  
XIAOJIA WANG ◽  
YONGZHAO ZHAN
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Feature Selection ◽  
Decision Tree ◽  
Classification Accuracy ◽  
Feature Selection Method ◽  
Speech Emotion Recognition ◽  
Recognition Method ◽  
Speech Corpus ◽  
Binary Decision Tree

In this paper, in order to improve the classification accuracy with features as few as possible, a new hierarchical recognition method based on an improved SVM decision tree and the layered feature selection method combining neural network with genetic algorithm are proposed. The improved SVM decision tree is constructed according to confusion degrees between two emotions or those between two emotion groups. The classifier in each node of the improved decision tree is a SVM. On the emotional speech corpus recorded by our workgroup including 7 emotions, with the features and parameters gotten by the method combining neural network with genetic algorithm, improved SVM decision tree, multi-SVM, SVM-based binary decision tree, the traditional SVM-based decision directed acyclic graph and HMM are evaluated respectively. The experiments reveal that, compared with the other four methods, the proposed method in this paper appears better classification accuracy with fewer features and less time.

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