Random forest and rotation forest ensemble methods for classification of epileptic EEG signals based on improved 1D‐LBP feature extraction

2020 ◽  
Author(s):  
Amirreza Geran Malek ◽  
Mojtaba Mansoori ◽  
Hesam Omranpour
Keyword(s):  
Feature Extraction ◽  
Random Forest ◽  
Ensemble Methods ◽  
Eeg Signals ◽  
Rotation Forest

Combined feature extraction method for classification of EEG signals

2016 ◽  
Vol 28 (11) ◽  
pp. 3153-3161 ◽  
Author(s):  
Yong Zhang ◽  
Xiaomin Ji ◽  
Bo Liu ◽  
Dan Huang ◽  
Fuding Xie ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Eeg Signals ◽  
Feature Extraction Method ◽  
Combined Feature

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.

Feature extraction using Pythagorean means for classification of epileptic EEG signals

2018 ◽  
Vol 28 (3) ◽  
pp. 243
Author(s):  
P.P. Muhammed Shanir ◽  
Sadaf Iqbal ◽  
Yusuf U. Khan ◽  
Omar Farooq
Keyword(s):  
Feature Extraction ◽  
Eeg Signals

Feature extraction method for classification of alertness and drowsiness states EEG signals

2020 ◽  
Vol 163 ◽  
pp. 107224 ◽  
Author(s):  
Varun Bajaj ◽  
Sachin Taran ◽  
Smith K. Khare ◽  
Abdulkadir Sengur
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Eeg Signals ◽  
Feature Extraction Method

scholarly journals Detection and Classification of Overlapping Cell Nuclei in Cytology Effusion Images Using a Double-Strategy Random Forest

2018 ◽  
Vol 8 (9) ◽  
pp. 1608 ◽  
Author(s):  
Khin Win ◽  
Somsak Choomchuay ◽  
Kazuhiko Hamamoto ◽  
Manasanan Raveesunthornkiat
Keyword(s):  
Feature Extraction ◽  
Random Forest ◽  
Performance Metrics ◽  
Superior Performance ◽  
New Combination ◽  
Final Decision ◽  
Cell Detection ◽  
Cell Nuclei ◽  
Dominant Feature

Due to the close resemblance between overlapping and cancerous nuclei, the misinterpretation of overlapping nuclei can affect the final decision of cancer cell detection. Thus, it is essential to detect overlapping nuclei and distinguish them from single ones for subsequent quantitative analyses. This paper presents a method for the automated detection and classification of overlapping nuclei from single nuclei appearing in cytology pleural effusion (CPE) images. The proposed system is comprised of three steps: nuclei candidate extraction, dominant feature extraction, and classification of single and overlapping nuclei. A maximum entropy thresholding method complemented by image enhancement and post-processing was employed for nuclei candidate extraction. For feature extraction, a new combination of 16 geometrical and 10 textural features was extracted from each nucleus region. A double-strategy random forest was performed as an ensemble feature selector to select the most relevant features, and an ensemble classifier to differentiate between overlapping nuclei and single ones using selected features. The proposed method was evaluated on 4000 nuclei from CPE images using various performance metrics. The results were 96.6% sensitivity, 98.7% specificity, 92.7% precision, 94.6% F1 score, 98.4% accuracy, 97.6% G-mean, and 99% area under curve. The computation time required to run the entire algorithm was just 5.17 s. The experiment results demonstrate that the proposed algorithm yields a superior performance to previous studies and other classifiers. The proposed algorithm can serve as a new supportive tool in the automated diagnosis of cancer cells from cytology images.

1D-local binary pattern based feature extraction for classification of epileptic EEG signals

2014 ◽  
Vol 243 ◽  
pp. 209-219 ◽  
Author(s):  
Yılmaz Kaya ◽  
Murat Uyar ◽  
Ramazan Tekin ◽  
Selçuk Yıldırım
Keyword(s):  
Feature Extraction ◽  
Local Binary Pattern ◽  
Eeg Signals
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