Supervised Machine Learning

Supervised machine learning focuses on training models to predict outcomes based on labeled data, aiming to build accurate and reliable predictive systems. Current research emphasizes improving model robustness to real-world data distribution shifts, exploring the limitations of using privileged information during training, and enhancing model interpretability through techniques like global sensitivity analysis and SHAP values. These advancements are crucial for various applications, from medical diagnostics and materials science to cybersecurity and social media analysis, improving decision-making across diverse fields.

Papers

April 6, 2022

Abusive and Threatening Language Detection in Urdu using Supervised Machine Learning and Feature Combinations
Muhammad Humayoun
Supervised Machine Learning Urdu Language Abusive Language Detection Feature Combination Urdu Dataset Threatening Language Detection

March 23, 2022

A Supervised Machine Learning Approach for Sequence Based Protein-protein Interaction (PPI) Prediction
Soumyadeep Debnath, Ayatullah Faruk Mollah
Human Prediction Supervised Machine Learning Protein Protein Interaction Interaction Datasets

March 22, 2022

Explainable Landscape Analysis in Automated Algorithm Performance Prediction
Risto Trajanov, Stefan Dimeski, Martin Popovski, Peter Korošec, Tome Eftimov
Machine Learning Algorithm Supervised Machine Learning Landscape Analysis

March 11, 2022

Sampling Bias Correction for Supervised Machine Learning: A Bayesian Inference Approach with Practical Applications
Max Sklar
Bayesian Inference Logistic Regression Practical Application Supervised Machine Learning Statistical Inference Sampling Bias Bayesian Inference Framework

March 9, 2022

The Cross-evaluation of Machine Learning-based Network Intrusion Detection Systems
Giovanni Apruzzese, Luca Pajola, Mauro Conti
Intrusion Detection Network Intrusion Detection Supervised Machine Learning Network Intrusion Detection System Cross Article Comparison

March 7, 2022

An STDP-Based Supervised Learning Algorithm for Spiking Neural Networks
Zhanhao Hu, Tao Wang, Xiaolin Hu
Neural Network Spiking Neural Network Multi Layer Supervised Machine Learning Dependent Plasticity

March 2, 2022

Chained Generalisation Bounds
Eugenio Clerico, Amitis Shidani, George Deligiannidis, Arnaud Doucet
Generalization Bound Generalization Error Information Theoretic Supervised Machine Learning Information Theoretic Generalization Bound

March 1, 2022

Molecular Dynamics of Polymer-lipids in Solution from Supervised Machine Learning
James Andrews, Olga Gkountouna, Estela Blaisten-Barojas
Solution Path Molecular Dynamic Supervised Machine Learning Soft Membrane

February 28, 2022

Supervised Machine Learning Algorithm for Detecting Consistency between Reported Findings and the Conclusions of Mammography Reports
Alexander Berdichevsky, Mor Peleg, Daniel L. Rubin
Supervised Machine Learning Mammography Report Intriguing Finding Consistency Check BI Rad

February 5, 2022

A Survey on Poisoning Attacks Against Supervised Machine Learning
Wenjun Qiu
Machine Learning Artificial Intelligence Timely Survey Supervised Learning Poisoning Attack Supervised Machine Learning

January 28, 2022

Learning Curves for Decision Making in Supervised Machine Learning -- A Survey
Felix Mohr, Jan N. van Rijn
Machine Learning Timely Survey Decision Making Model Selection Supervised Machine Learning Learning Curve Parametric Curve

January 14, 2022

Corrigendum and addendum to: How Populist are Parties? Measuring Degrees of Populism in Party Manifestos Using Supervised Machine Learning
Jessica Di Cocco, Bernardo Monechi
Language Correction Supervised Machine Learning Data Labeling Random Label Political Information Political Party Party Manifesto Populist Stance

November 10, 2021

Deducing Optimal Classification Algorithm for Heterogeneous Fabric
Omar Alfarisi, Zeyar Aung, Mohamed Sassi
Practical Algorithm Random Forest Supervised Machine Learning Optimal Classifier Optimal Learning Heterogeneous Carbonate Rock Network Fabric

November 4, 2021

Predictive Machine Learning of Objective Boundaries for Solving COPs
Helge Spieker, Arnaud Gotlieb
Constraint Programming Supervised Machine Learning Constraint Optimization Problem

Supervised Machine Learning

Papers

Abusive and Threatening Language Detection in Urdu using Supervised Machine Learning and Feature Combinations

A Supervised Machine Learning Approach for Sequence Based Protein-protein Interaction (PPI) Prediction

Explainable Landscape Analysis in Automated Algorithm Performance Prediction

Sampling Bias Correction for Supervised Machine Learning: A Bayesian Inference Approach with Practical Applications

The Cross-evaluation of Machine Learning-based Network Intrusion Detection Systems

An STDP-Based Supervised Learning Algorithm for Spiking Neural Networks

Chained Generalisation Bounds

Molecular Dynamics of Polymer-lipids in Solution from Supervised Machine Learning

Supervised Machine Learning Algorithm for Detecting Consistency between Reported Findings and the Conclusions of Mammography Reports

A Survey on Poisoning Attacks Against Supervised Machine Learning

Learning Curves for Decision Making in Supervised Machine Learning -- A Survey

Corrigendum and addendum to: How Populist are Parties? Measuring Degrees of Populism in Party Manifestos Using Supervised Machine Learning

Deducing Optimal Classification Algorithm for Heterogeneous Fabric

Predictive Machine Learning of Objective Boundaries for Solving COPs