Classification of Textual Sentiment Using Ensemble Technique

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Md. Mashiur Rahaman Mamun ◽  
Omar Sharif ◽  
Mohammed Moshiul Hoque
Keyword(s):  
Ensemble Technique

Hybrid Ensemble Learning Methods for Classification of Microarray Data

2020 ◽  
pp. 707-725
Author(s):  
Sujata Dash
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Microarray Data ◽  
Classification Model ◽  
Rotation Forest ◽  
Ensemble Technique ◽  
Basic Characteristics ◽  
Microarray Datasets ◽  
Feature Selection Techniques

Efficient classification and feature extraction techniques pave an effective way for diagnosing cancers from microarray datasets. It has been observed that the conventional classification techniques have major limitations in discriminating the genes accurately. However, such kind of problems can be addressed by an ensemble technique to a great extent. In this paper, a hybrid RotBagg ensemble framework has been proposed to address the problem specified above. This technique is an integration of Rotation Forest and Bagging ensemble which in turn preserves the basic characteristics of ensemble architecture i.e., diversity and accuracy. Three different feature selection techniques are employed to select subsets of genes to improve the effectiveness and generalization of the RotBagg ensemble. The efficiency is validated through five microarray datasets and also compared with the results of base learners. The experimental results show that the correlation based FRFR with PCA-based RotBagg ensemble form a highly efficient classification model.

scholarly journals An Efficient Traffic Incident Detection and Classification Framework by Leveraging the Efficacy of Model Stacking

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Zafar Iqbal ◽  
Majid I. Khan ◽  
Shahid Hussain ◽  
Asad Habib
Keyword(s):  
Smart Cities ◽  
Transportation Systems ◽  
Wilcoxon Test ◽  
Incident Management ◽  
Incident Detection ◽  
Incident Type ◽  
Ensemble Technique ◽  
Traffic Incident ◽  
Type Classification

Automatic incident detection (AID) plays a vital role among all the safety-critical applications under the parasol of Intelligent Transportation Systems (ITSs) to provide timely information to passengers and other stakeholders (hospitals and rescue, police, and insurance departments) in smart cities. Moreover, accurate classification of these incidents with respect to type and severity assists the Traffic Incident Management Systems (TIMSs) and stakeholders in devising better plans for incident site management and avoiding secondary incidents. Most of the AID systems presented in the literature are incident type-specific, i.e., either they are designed for the detection of accident or congestion. While traveling along the road, one may come across different types of traffic incidents, such as accidents, congestion, and reckless driving. This necessitates that the AID system detects and classifies not only all the popular traffic incident types, but severity as well that is associated with these incidents. Therefore, this study aims to propose an efficient incident detection and classification (E-IDC) framework for smart cities, by incorporating the efficacy of model stacking, to classify the incidents with respect to their types and severity levels. The experimental results showed that the proposed E-IDC framework achieved performance gains of 5%–56% in terms of incident severity classification and 1%–14% in terms of incident type classification when applied with different classifiers. We have also applied the Wilcoxon test to benchmark the performance of our proposed framework that reflects the significance of our approach over existing individual incident predictors in terms of severity and type classification. Moreover, it has been observed that the proposed E-IDC framework outperforms the existing ensemble technique, such as XGBoost used for the classification of incidents.

Hybrid Ensemble Learning Methods for Classification of Microarray Data

2016 ◽  
pp. 17-36
Author(s):  
Sujata Dash
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Microarray Data ◽  
Classification Model ◽  
Rotation Forest ◽  
Ensemble Technique ◽  
Basic Characteristics ◽  
Microarray Datasets ◽  
Feature Selection Techniques

Efficient classification and feature extraction techniques pave an effective way for diagnosing cancers from microarray datasets. It has been observed that the conventional classification techniques have major limitations in discriminating the genes accurately. However, such kind of problems can be addressed by an ensemble technique to a great extent. In this paper, a hybrid RotBagg ensemble framework has been proposed to address the problem specified above. This technique is an integration of Rotation Forest and Bagging ensemble which in turn preserves the basic characteristics of ensemble architecture i.e., diversity and accuracy. Three different feature selection techniques are employed to select subsets of genes to improve the effectiveness and generalization of the RotBagg ensemble. The efficiency is validated through five microarray datasets and also compared with the results of base learners. The experimental results show that the correlation based FRFR with PCA-based RotBagg ensemble form a highly efficient classification model.

scholarly journals CluSem: Accurate Clustering-based Ensemble Method to Predict Motor Imagery Tasks from Multi-channel EEG Data

2021 ◽  
Author(s):  
Md Ochiuddin Miah ◽  
Rafsanjani Muhammod ◽  
Khondaker Abdullah Al Mamun ◽  
Dewan Md. Farid ◽  
Shiu Kumar ◽  
...  
Keyword(s):  
Real Time ◽  
Motor Imagery ◽  
Classification Performance ◽  
Input Devices ◽  
Ensemble Technique ◽  
Source Codes ◽  
Eeg Data ◽  
Time Motor ◽  
Bio Engineering

Background: The classification of motor imagery electroencephalogram (MI-EEG) is a pivotal task in the biosignal classification process in brain-computer interface (BCI) applications. Currently, this bio-engineering-based technology is being employed by researchers in various fields to develop cutting-edge applications. The classification of real-time MI-EEG signals is the most challenging task in these applications. The prediction performance of the existing classification methods is still limited due to the high dimensionality and dynamic behaviors of the real-time EEG data. Proposed Method: To enhance the classification performance of real-time BCI applications, this paper presents a new clustering-based ensemble technique called CluSem to mitigate this problem. We also develop a new brain game called CluGame using this method to evaluate the classification performance of real-time motor imagery movements. In this game, real-time EEG signal classification and prediction tabulation through animated balls are controlled via threads. By playing this game, users can control the movements of the balls via the brain signals of motor imagery movements without using any traditional input devices. Results: Our results demonstrate that CluSem is able to improve the classification accuracy between 5% and 15% compared to the existing methods on our collected as well as the publicly available EEG datasets. The source codes used to implement CluSem and CluGame are publicly available at https://github.com/MdOchiuddinMiah/MI-BCI_ML.

scholarly journals Ensemble Framework of Deep CNNs for Diabetic Retinopathy Detection

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Gao Jinfeng ◽  
Sehrish Qummar ◽  
Zhang Junming ◽  
Yao Ruxian ◽  
Fiaz Gul Khan
Keyword(s):  
Diabetic Retinopathy ◽  
State Of The Art ◽  
Imbalanced Datasets ◽  
Blurred Vision ◽  
Ensemble Technique ◽  
Early Stages ◽  
Processing Data ◽  
Deep Cnn ◽  
Graphical Processing

Diabetic retinopathy (DR) is an eye disease that damages the blood vessels of the eye. DR causes blurred vision or it may lead to blindness if it is not detected in early stages. DR has five stages, i.e., 0 normal, 1 mild, 2 moderate, 3 severe, and 4 PDR. Conventionally, many hand-on projects of computer vision have been applied to detect DR but cannot code the intricate underlying features. Therefore, they result in poor classification of DR stages, particularly for early stages. In this research, two deep CNN models were proposed with an ensemble technique to detect all the stages of DR by using balanced and imbalanced datasets. The models were trained with Kaggle dataset on a high-end Graphical Processing data. Balanced dataset was used to train both models, and we test these models with balanced and imbalanced datasets. The result shows that the proposed models detect all the stages of DR unlike the current methods and perform better compared to state-of-the-art methods on the same Kaggle dataset.

Multi-Label Classification of Small Samples Using an Ensemble Technique

2018 ◽  
Author(s):  
Amirreza Mahdavi-Shahri ◽  
Jamil Karimian ◽  
Azadeh Javadi ◽  
Mahboobeh Houshmand
Keyword(s):  
Small Samples ◽  
Ensemble Technique

scholarly journals Ensemble-Guided Model for Performance Enhancement in Model-Complexity-Limited Acoustic Scene Classification

2021 ◽  
Vol 12 (1) ◽  
pp. 44
Author(s):  
Seokjin Lee ◽  
Minhan Kim ◽  
Seunghyeon Shin ◽  
Seungjae Baek ◽  
Sooyoung Park ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Performance Enhancement ◽  
Learning Performance ◽  
Model Complexity ◽  
Scene Classification ◽  
Ensemble Technique ◽  
Enhancement Method ◽  
Model Size ◽  
Teacher Model

In recent acoustic scene classification (ASC) models, various auxiliary methods to enhance performance have been applied, e.g., subsystem ensembles and data augmentations. Particularly, the ensembles of several submodels may be effective in the ASC models, but there is a problem with increasing the size of the model because it contains several submodels. Therefore, it is hard to be used in model-complexity-limited ASC tasks. In this paper, we would like to find the performance enhancement method while taking advantage of the model ensemble technique without increasing the model size. Our method is proposed based on a mean-teacher model, which is developed for consistency learning in semi-supervised learning. Because our problem is supervised learning, which is different from the purpose of the conventional mean-teacher model, we modify detailed strategies to maximize the consistency learning performance. To evaluate the effectiveness of our method, experiments were performed with an ASC database from the Detection and Classification of Acoustic Scenes and Events 2021 Task 1A. The small-sized ASC model with our proposed method improved the log loss performance up to 1.009 and the F1-score performance by 67.12%, whereas the vanilla ASC model showed a log loss of 1.052 and an F1-score of 65.79%.

scholarly journals Prediction of Motor Imagery Tasks from Multi-Channel EEG Data for Brain-Computer Interface Applications

2020 ◽  
Author(s):  
Md. Ochiuddin Miah ◽  
Md. Mahfuzur Rahman ◽  
Rafsanjani Muhammod ◽  
Dewan Md. Farid
Keyword(s):  
Real Time ◽  
Motor Imagery ◽  
Support Vector ◽  
Eeg Signal ◽  
Ensemble Technique ◽  
Brain Signals ◽  
Eeg Data ◽  
Bio Engineering ◽  
The Brain

AbstractThe classification of motor imagery electroencephalogram (MI-EEG) is a pivotal part of the biosignal classification in the brain-computer interface (BCI) applications. Currently, this bio-engineering based technology is being employed by researchers in various fields to develop cutting edge applications. The classification of real-time MI-EEG signal is the core computing and challenging task in these applications. It is well-known that the existing classification methods are not so accurate due to the high dimensionality and dynamic behaviors of the real-time EEG data. To improve the classification performance of real-time BCI applications, this paper presents a clustering-based ensemble technique and a developed brain game that distinguishes different human thoughts. At first, we have gathered the brain signals, extracted and selected informative features from these signals to generate training and testing sets. After that, we have constructed several classifiers using Artificial Neural Network (ANN), Support Vector Machine (SVM), naïve Bayes, Decision Tree (DT), Random Forest, Bagging, AdaBoost and compared the performance of these existing approaches with suggested clustering-based ensemble technique. On average, the proposed ensemble technique improved the classification accuracy of roughly 5 to 15% compared to the existing methods. Finally, we have developed the targeted brain game employing our suggested ensemble technique. In this game, real-time EEG signal classification and prediction tabulation through animated ball are controlled via threads. By playing this game, users can control the movements of the balls via the brain signals of motor imagery movements without using any traditional input devices. All relevant codes are available via open repository at: https://github.com/mrzResearchArena/MI-EEG.

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