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

November 5, 2024

SUDS: A Strategy for Unsupervised Drift Sampling
Christofer Fellicious, Lorenz Wendlinger, Mario Gancarski, Jelena Mitrovic, Michael Granitzer
Concept Drift Strategy Logic Drift Detection Supervised Machine Learning

October 27, 2024

Predicting Mortality and Functional Status Scores of Traumatic Brain Injury Patients using Supervised Machine Learning
Lucas Steinmetz, Shivam Maheshwari, Garik Kazanjian, Abigail Loyson, Tyler Alexander, Venkat Margapuri, C. Nataraj
Logistic Regression Supervised Machine Learning Mortality Prediction Year Specific Mortality Surface Functional Outcome Traumatic Brain Injury

October 14, 2024

A Human-Centered Approach for Improving Supervised Learning
Shubhi Bansal, Atharva Tendulkar, Nagendra Kumar
Machine Learning Supervised Learning Human Centric Supervised Machine Learning Stacking Ensemble Supervised Learning Problem

October 5, 2024

IT$^3$: Idempotent Test-Time Training
Nikita Durasov, Assaf Shocher, Doruk Oner, Gal Chechik, Alexei A. Efros, Pascal Fua
Supervised Machine Learning Test Time Training Training Distribution

August 26, 2024

Rethinking Knowledge Transfer in Learning Using Privileged Information
Danil Provodin, Bram van den Akker, Christina Katsimerou, Maurits Kaptein, Mykola Pechenizkiy
LeArning Abstract Scientific Inference Knowledge Transfer Inductive Bias Supervised Machine Learning Privileged Information

July 19, 2024

Enhancing Variable Importance in Random Forests: A Novel Application of Global Sensitivity Analysis
Giulia Vannucci, Roberta Siciliano, Andrea Saltelli
Machine Learning High Dimensional Random Forest Supervised Machine Learning Variable Importance Novel Application Global Sensitivity Analysis

June 21, 2024

Detecting and Classifying Flares in High-Resolution Solar Spectra with Supervised Machine Learning
Nicole Hao, Laura Flagg, Ray Jayawardhana
Data Detection Supervised Machine Learning Solar Flare Stellar Spectrum Flare Artifact Flare Prediction

June 18, 2024

Structured Prediction in Online Learning
Pierre Boudart, Alessandro Rudi, Pierre Gaillard
Online Learning Supervised Machine Learning Structured Prediction Optimal Algorithm Supervised Learning Problem

April 15, 2024

Classification Tree-based Active Learning: A Wrapper Approach
Ashna Jose, Emilie Devijver, Massih-Reza Amini, Noel Jakse, Roberta Poloni
Classification Code Active Learning Supervised Machine Learning Classification Tree

April 6, 2024

Predictive Modeling for Breast Cancer Classification in the Context of Bangladeshi Patients: A Supervised Machine Learning Approach with Explainable AI
Taminul Islam, Md. Alif Sheakh, Mst. Sazia Tahosin, Most. Hasna Hena, Shopnil Akash, Yousef A. Bin Jardan, Gezahign Fentahun Wondmie, Hiba-Allah Nafidi, Mohammed Bourhia
Machine Learning Explainable AI Decision Tree Predictive Model Breast Cancer Logistic Regression Supervised Machine Learning Breast Cancer Classification

February 27, 2024

Supervised machine learning for microbiomics: bridging the gap between current and best practices
Natasha K. Dudek, Mariam Chakhvadze, Saba Kobakhidze, Omar Kantidze, Yuriy Gankin
Machine Learning Algorithm Best Practice Supervised Machine Learning Microbiome Datasets Microbiome Research

February 6, 2024

Employee Turnover Analysis Using Machine Learning Algorithms
Mahyar Karimi, Kamyar Seyedkazem Viliyani
Predictive Model Machine Learning Algorithm Supervised Machine Learning Employee Attrition

November 21, 2023

Classification of Instagram fake users using supervised machine learning algorithms
Vertika Singh, Naman Tolasaria, Patel Meet Alpeshkumar, Shreyash Bartwal
Classification Code Social Network Supervised Machine Learning Fake User Context Impersonation

October 20, 2023

An Exploratory Study on Simulated Annealing for Feature Selection in Learning-to-Rank
Mohd. Sayemul Haque, Md. Fahim, Muhammad Ibrahim
Feature Selection Exploratory Study Supervised Machine Learning Learning to Rank Simulated Annealing

October 16, 2023

Advantages of Machine Learning in Bus Transport Analysis
Amirsadegh Roshanzamir
Machine Learning Supervised Learning Supervised Machine Learning Public Transit Advantage Feedback Route Recommendation

October 10, 2023

Assessing the Impact of a Supervised Classification Filter on Flow-based Hybrid Network Anomaly Detection
Dominik Macko, Patrik Goldschmidt, Peter Pištek, Daniela Chudá
Global Impact Supervised Machine Learning Based Anomaly Detection Anomaly Detector Network Anomaly Detection

September 24, 2023

From Cluster Assumption to Graph Convolution: Graph-based Semi-Supervised Learning Revisited
Zheng Wang, Hongming Ding, Li Pan, Jianhua Li, Zhiguo Gong, Philip S. Yu
Convolutional Neural Network Semi Supervised Graph Convolution Supervised Machine Learning New Event Cluster

September 13, 2023

Supervised Machine Learning and Physics based Machine Learning approach for prediction of peak temperature distribution in Additive Friction Stir Deposition of Aluminium Alloy
Akshansh Mishra
Human Prediction Machine Learning Approach Supervised Machine Learning Microstructure Model Laser Powder Bed Fusion Energy Deposition Friction Stir

August 23, 2023

Ensembling Uncertainty Measures to Improve Safety of Black-Box Classifiers
Tommaso Zoppi, Andrea Ceccarelli, Andrea Bondavalli
Machine Learning Black Box Human SAFETY Multi Class Classification Supervised Machine Learning Uncertainty Measure

August 17, 2023

Explainable AI for tool wear prediction in turning
Saleh Valizadeh Sotubadi, Rui Liu, Vinh Neguyen
Machine Learning Human Prediction Explainable AI BRIO Tool Supervised Machine Learning Machining Feature

Supervised Machine Learning

Papers

SUDS: A Strategy for Unsupervised Drift Sampling

Predicting Mortality and Functional Status Scores of Traumatic Brain Injury Patients using Supervised Machine Learning

A Human-Centered Approach for Improving Supervised Learning

IT$^3$: Idempotent Test-Time Training

Rethinking Knowledge Transfer in Learning Using Privileged Information

Enhancing Variable Importance in Random Forests: A Novel Application of Global Sensitivity Analysis

Detecting and Classifying Flares in High-Resolution Solar Spectra with Supervised Machine Learning

Structured Prediction in Online Learning

Classification Tree-based Active Learning: A Wrapper Approach

Predictive Modeling for Breast Cancer Classification in the Context of Bangladeshi Patients: A Supervised Machine Learning Approach with Explainable AI

Supervised machine learning for microbiomics: bridging the gap between current and best practices

Employee Turnover Analysis Using Machine Learning Algorithms

Classification of Instagram fake users using supervised machine learning algorithms

An Exploratory Study on Simulated Annealing for Feature Selection in Learning-to-Rank

Advantages of Machine Learning in Bus Transport Analysis

Assessing the Impact of a Supervised Classification Filter on Flow-based Hybrid Network Anomaly Detection

From Cluster Assumption to Graph Convolution: Graph-based Semi-Supervised Learning Revisited

Supervised Machine Learning and Physics based Machine Learning approach for prediction of peak temperature distribution in Additive Friction Stir Deposition of Aluminium Alloy

Ensembling Uncertainty Measures to Improve Safety of Black-Box Classifiers

Explainable AI for tool wear prediction in turning