A Novel Approach to Ensemble Classifiers: FsBoost-Based Subspace Method

Noor, Adeeb; Uçar, Muhammed Kürşad; Polat, Kemal; Assiri, Abdullah; Nour, Redhwan

doi:10.1155/2020/8571712

Cited by 3 publications

(2 citation statements)

References 31 publications

(41 reference statements)

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“…While working with the features obtained by feature extraction, it can also been seen that feature selection is performed using different metrics. As an example, in a study [17], features were selected according to the F-Score. The authors proposed a new ensemble learning algorithm, called the F-Score Subspace Method (FsBoost), by emphasizing the importance of feature selection.…”

Section: Related Workmentioning

confidence: 99%

Support Vector Machine Chains with a Novel Tournament Voting

et al. 2023

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Support vector machine (SVM) algorithms have been widely used for classification in many different areas. However, the use of a single SVM classifier is limited by the advantages and disadvantages of the algorithm. This paper proposes a novel method, called support vector machine chains (SVMC), which involves chaining together multiple SVM classifiers in a special structure, such that each learner is constructed by decrementing one feature at each stage. This paper also proposes a new voting mechanism, called tournament voting, in which the outputs of classifiers compete in groups, the common result in each group gradually moves to the next round, and, at the last round, the winning class label is assigned as the final prediction. Experiments were conducted on 14 real-world benchmark datasets. The experimental results showed that SVMC (88.11%) achieved higher accuracy than SVM (86.71%) on average thanks to the feature selection, sampling, and chain structure combined with multiple models. Furthermore, the proposed tournament voting demonstrated higher performance than the standard majority voting in terms of accuracy. The results also showed that the proposed SVMC method outperformed the state-of-the-art methods with a 6.88% improvement in average accuracy.

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Section: Related Workmentioning

confidence: 99%

Support Vector Machine Chains with a Novel Tournament Voting

et al. 2023

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“…If the number of classifiers is, in fact, the average value of the classifier's decision, it is rounded off, and the ensemble classifier decision is determined. All feature vectors are applied by this process [59]. We used the ensemble (subspace KNN) method using six dimensions subspace and learner nearest neighbors using 30 learners.…”

Section: Machine Learning (Ml) Stagementioning

confidence: 99%

Minimum Mapping from EMG Signals at Human Elbow and Shoulder Movements into Two DoF Upper-Limb Robot with Machine Learning

et al. 2021

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This research focuses on the minimum process of classifying three upper arm movements (elbow extension, shoulder extension, combined shoulder and elbow extension) of humans with three electromyography (EMG) signals, to control a 2-degrees of freedom (DoF) robotic arm. The proposed minimum process consists of four parts: time divisions of data, Teager–Kaiser energy operator (TKEO), the conventional EMG feature extraction (i.e., the mean absolute value (MAV), zero crossings (ZC), slope-sign changes (SSC), and waveform length (WL)), and eight major machine learning models (i.e., decision tree (medium), decision tree (fine), k-Nearest Neighbor (KNN) (weighted KNN, KNN (fine), Support Vector Machine (SVM) (cubic and fine Gaussian SVM), Ensemble (bagged trees and subspace KNN). Then, we compare and investigate 48 classification models (i.e., 47 models are proposed, and 1 model is the conventional) based on five healthy subjects. The results showed that all the classification models achieved accuracies ranging between 74–98%, and the processing speed is below 40 ms and indicated acceptable controller delay for robotic arm control. Moreover, we confirmed that the classification model with no time division, with TKEO, and with ensemble (subspace KNN) had the best performance in accuracy rates at 96.67, recall rates at 99.66, and precision rates at 96.99. In short, the combination of the proposed TKEO and ensemble (subspace KNN) plays an important role to achieve the EMG classification.

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