Exploring the mel scale features using supervised learning classifiers for emotion classification

2021 ◽  
Vol 6 (3) ◽  
pp. 232
Author(s):  
Kalpana Rangra ◽  
Monit Kapoor
Keyword(s):  
Supervised Learning ◽  
Emotion Classification ◽  
Learning Classifiers

scholarly journals An Accurate and Easy to Interpret Binary Classifier Based on Association Rules Using Implication Intensity and Majority Vote

Mathematics ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 1315
Author(s):  
Souhila Ghanem ◽  
Raphaël Couturier ◽  
Pablo Gregori
Keyword(s):  
Neural Networks ◽  
Open Access ◽  
Supervised Learning ◽  
Association Rules ◽  
Majority Vote ◽  
Binary Classifier ◽  
Learning Classifiers

In supervised learning, classifiers range from simpler, more interpretable and generally less accurate ones (e.g., CART, C4.5, J48) to more complex, less interpretable and more accurate ones (e.g., neural networks, SVM). In this tradeoff between interpretability and accuracy, we propose a new classifier based on association rules, that is to say, both easy to interpret and leading to relevant accuracy. To illustrate this proposal, its performance is compared to other widely used methods on six open access datasets.

scholarly journals Supervised Learning Classifiers for Electrical Impedance-based Bladder State Detection

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Eoghan Dunne ◽  
Adam Santorelli ◽  
Brian McGinley ◽  
Geraldine Leader ◽  
Martin O’Halloran ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Electrical Impedance ◽  
Learning Classifiers ◽  
State Detection

scholarly journals Supervised learning techniques to predict compounds in pathway modules based on molecular properties

2021 ◽  
Author(s):  
Hayat Ali Shah
Keyword(s):  
Machine Learning ◽  
Supervised Learning ◽  
Metabolic Pathway ◽  
Molecular Properties ◽  
Grid Search ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Learning Techniques ◽  
Pathway Module ◽  
Prediction Problems

<div># Machine learning Classifiers for prediction of Pathway module & it classes </div><div>We use SMILES representation of query molecules to generate relevant fingerprints, which are then fed to the machine learning classifiers ETC for producing binary labels corresponding pathway module & its classes. The details of the works are described in our paper.</div><div>A dataset of 6597 downloaded from KEGG, 4612 compounds either belong or not to Pathway module in metabolic pathway the remaining 1985 compounds belong to module classes prediction problems </div><div>### Requirements</div><div>*Chemoinformatics tools</div><div>* Python</div><div>* scikit-learn</div><div>* RDKit</div><div>* Jupyter Notebook</div><div>### Usage</div><div>We provide two folder containing Classifiers files,grid search for optimization of hyperparameters, and datasets(module, module classes</div>

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