Least squares twin SVM decision tree for multi-class classification

2016 ◽  
Author(s):  
Qing Yu ◽  
Lihui Wang
Keyword(s):  
Decision Tree ◽  
Least Squares ◽  
Multi Class Classification

Multi-class classification with probabilistic discriminant partial least squares (p-DPLS)

2010 ◽  
Vol 664 (1) ◽  
pp. 27-33 ◽  
Author(s):  
Néstor F. Pérez ◽  
Joan Ferré ◽  
Ricard Boqué
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Multi Class Classification

scholarly journals A Pattern Recognition Diagnostic Model to Restore and Emulate Knee Mobility

2021 ◽  
Author(s):  
Bayu Sukmanto ◽  
Sadaira Packer ◽  
Muhammad Gulfam ◽  
David Hollinger
Keyword(s):  
Pattern Recognition ◽  
Random Forest ◽  
Decision Tree ◽  
Least Squares ◽  
Real Time ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Secondary Outcome ◽  
Least Squares Regression ◽  
Tree Classifier

Electromyography (EMG) is an electrical voltage potential linked to muscle contraction, resulting in human joint motion, such as knee flexion. Knee injuries, such as knee osteoarthritis (KOA), disrupt functional mobility of the knee joint and subsequently atrophy the muscles controlling knee movement during activities of daily living (ADL). Consequently, weakened muscles exhibiting deteriorated EMG signal fidelity are hypothesized to have discernible signal patterns from a healthy individual's EMG signals. Pattern recognition algorithms are useful for mapping a set of complex inputs (EMG signals and knee angles) to classify knee health status (injured vs. healthy). A secondary outcome is to predict future knee angles from previous input signals to inform a robotic knee exoskeleton to apply real-time torque assistance to a patient during ADL. A Decision Tree Classifier, Random Forest, Naive Bayes, and a Feed-Forward Neural Network (Fully Connected) were used for binary classification (healthy vs. injured). Partial Least Squares Regression, Decision Tree Regressor, and XGBoost were used to predict future joint angles for the regression task (knee angle prediction). Overall, the Random Forest Classifier had the best overall classification performance. XGBoost and Decision Tree Regression performed the best among regression algorithms for predicting real-time angles during walking while Partial Least Squares Regression performed the best during the standing tasks. In summary, our Machine Learning methods are useful for assisting clinicians and patients during physical rehabilitation by providing quantitative insight into the patient's neuromuscular control of the knee.

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