Diagnosis of Neuromuscular Disorders Using DT-CWT and Rotation Forest Ensemble Classifier

2020 ◽  
Vol 69 (5) ◽  
pp. 1940-1947 ◽  
Author(s):  
Abdulhamit Subasi
Keyword(s):  
Neuromuscular Disorders ◽  
Ensemble Classifier ◽  
Rotation Forest

scholarly journals Virtual Screening of Drug Likeness using Tree Based Ensemble Classifier

2018 ◽  
Vol 11 (3) ◽  
pp. 1513-1519 ◽  
Author(s):  
R. Ani ◽  
Roshini Manohar ◽  
Gayathri Anil ◽  
O.S. Deepa
Keyword(s):  
Drug Discovery ◽  
Virtual Screening ◽  
Molecular Descriptor ◽  
Computational Procedure ◽  
Ensemble Classifier ◽  
Support Vector ◽  
Classification Algorithms ◽  
Discovery Process ◽  
Drug Discovery Process ◽  
Rotation Forest

In earlier years, the Drug discovery process took years to identify and process a Drug. It takes a normal of 12 years for a Drug to travel from the research lab to the patient. With the introduction of Machine Learning in Drug discovery, the whole process turned out to be simple. The utilization of computational tools in the early stages of Drug development has expanded in recent decades. A computational procedure carried out in Drug discovery process is Virtual Screening (VS). VS are used to identify the compounds which can bind to a Drug target. The preliminary process before analyzing the bonding of ligand and drug protein target is the prediction of drug likeness of compounds. The main objective of this study is to predict Drug likeness properties of Drug compounds based on molecular descriptor information using Tree based ensembles. In this study, many classification algorithms are analyzed and the accuracy for the prediction of drug likeness is calculated. The study shows that accuracy of rotation forest outperforms the accuracy of other classification algorithms in the prediction of drug likeness of chemical compounds. The measured accuracies of the Rotation Forest, Random Forest, Support Vector Machines, KNN, Decision Tree and Naïve Bayes are 98%, 97%, 94.8%, 92.8%, 91.4%, 89.5% respectively.

scholarly journals Ens-PPI: A Novel Ensemble Classifier for Predicting the Interactions of Proteins Using Autocovariance Transformation from PSSM

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Zhen-Guo Gao ◽  
Lei Wang ◽  
Shi-Xiong Xia ◽  
Zhu-Hong You ◽  
Xin Yan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Protein Interactions ◽  
Biological Activities ◽  
Ensemble Classifier ◽  
Prediction Performance ◽  
Protein Protein Interactions ◽  
Rotation Forest ◽  
Proposed Model ◽  
Protein Amino Acids ◽  
Scoring Matrix

Protein-Protein Interactions (PPIs) play vital roles in most biological activities. Although the development of high-throughput biological technologies has generated considerable PPI data for various organisms, many problems are still far from being solved. A number of computational methods based on machine learning have been developed to facilitate the identification of novel PPIs. In this study, a novel predictor was designed using the Rotation Forest (RF) algorithm combined with Autocovariance (AC) features extracted from the Position-Specific Scoring Matrix (PSSM). More specifically, the PSSMs are generated using the information of protein amino acids sequence. Then, an effective sequence-based features representation, Autocovariance, is employed to extract features from PSSMs. Finally, the RF model is used as a classifier to distinguish between the interacting and noninteracting protein pairs. The proposed method achieves promising prediction performance when performed on the PPIs ofYeast,H.pylori, andindependent datasets. The good results show that the proposed model is suitable for PPIs prediction and could also provide a useful supplementary tool for solving other bioinformatics problems.

scholarly journals The Drug Design for Diabetes Mellitus type II using Rotation Forest Ensemble Classifier

2021 ◽  
Vol 179 ◽  
pp. 161-168
Author(s):  
Nadya Asanul Husna ◽  
Alhadi Bustamam ◽  
Arry Yanuar ◽  
Devvi Sarwinda
Keyword(s):  
Diabetes Mellitus ◽  
Drug Design ◽  
Ensemble Classifier ◽  
Diabetes Mellitus Type ◽  
Type Ii ◽  
Rotation Forest ◽  
Diabetes Mellitus Type Ii

scholarly journals Comparison of Bagging and Boosting Ensemble Machine Learning Methods for Automated EMG Signal Classification

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Emine Yaman ◽  
Abdulhamit Subasi
Keyword(s):  
Machine Learning ◽  
Neuromuscular Disorders ◽  
Real Life ◽  
Kappa Statistic ◽  
Ensemble Classifier ◽  
Machine Learning Algorithms ◽  
Ensemble Classifiers ◽  
Learning Methods ◽  
Emg Signal ◽  
Ensemble Machine Learning

The neuromuscular disorders are diagnosed using electromyographic (EMG) signals. Machine learning algorithms are employed as a decision support system to diagnose neuromuscular disorders. This paper compares bagging and boosting ensemble learning methods to classify EMG signals automatically. Even though ensemble classifiers’ efficacy in relation to real-life issues has been presented in numerous studies, there are almost no studies which focus on the feasibility of bagging and boosting ensemble classifiers to diagnose the neuromuscular disorders. Therefore, the purpose of this paper is to assess the feasibility of bagging and boosting ensemble classifiers to diagnose neuromuscular disorders through the use of EMG signals. It should be understood that there are three steps to this method, where the step number one is to calculate the wavelet packed coefficients (WPC) for every type of EMG signal. After this, it is necessary to calculate statistical values of WPC so that the distribution of wavelet coefficients could be demonstrated. In the last step, an ensemble classifier used the extracted features as an input of the classifier to diagnose the neuromuscular disorders. Experimental results showed the ensemble classifiers achieved better performance for diagnosis of neuromuscular disorders. Results are promising and showed that the AdaBoost with random forest ensemble method achieved an accuracy of 99.08%, F-measure 0.99, AUC 1, and kappa statistic 0.99.

scholarly journals EEG-based emotion recognition using tunable Q wavelet transform and rotation forest ensemble classifier

2021 ◽  
Vol 68 ◽  
pp. 102648
Author(s):  
Abdulhamit Subasi ◽  
Turker Tuncer ◽  
Sengul Dogan ◽  
Dahiru Tanko ◽  
Unal Sakoglu
Keyword(s):  
Wavelet Transform ◽  
Emotion Recognition ◽  
Ensemble Classifier ◽  
Rotation Forest

scholarly journals A Stacking-based Ensemble Learning Method for Outlier Detection

2021 ◽  
Author(s):  
Abdul Ahad Abro
Keyword(s):  
Machine Learning ◽  
Outlier Detection ◽  
Area Under Curve ◽  
Binary Classification ◽  
Ensemble Classifier ◽  
Performance Criteria ◽  
Learning Problems ◽  
Learning Method ◽  
Rotation Forest ◽  
Research Areas

Abstract Outlier detection is considered as one of the crucial research areas for data mining. Many methods have been studied widely and utilized for achieving better results in outlier detection from existing literature; however, the effects of these few ways are inadequate. In this paper, a stacking-based ensemble classifier has been proposed along with four base learners (namely, Rotation Forest, Random Forest, Bagging and Boosting) and a Meta-learner (namely, Logistic Regression) to progress the outlier detection performance. The proposedmechanism is evaluated on five datasets from the ODDS library by adopting five performance criteria. The experimental outcomes demonstrate that the proposed method outperforms than the conventional ensemble approaches concerning the accuracy, AUC (Area Under Curve), precision, recall and Fmeasure values.This method can be used for image recognition and machine learning problems, such as binary classification.

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