Learning Classifier

Learning classifiers focuses on developing algorithms that accurately categorize data into predefined classes, a fundamental task in machine learning. Current research emphasizes improving classifier performance on challenging datasets, including those with imbalanced class distributions, noisy labels, or high dimensionality, often employing techniques like ensemble methods (e.g., bagging, random forests), and incorporating label uncertainty. These advancements are crucial for various applications, ranging from medical diagnosis and image recognition to robotics and explainable AI, where robust and reliable classification is paramount.

Papers

October 4, 2024

Improving Online Bagging for Complex Imbalanced Data Stream
Bartosz Przybyl, Jerzy Stefanowski
Imbalanced Datasets Online Classification Learning Classifier Minority Class Evolutionary Bagging

September 24, 2024

Learning with Confidence: Training Better Classifiers from Soft Labels
Sjoerd de Vries, Dirk Thierens
LeArning Abstract Soft Label High Confidence Label Uncertainty Learning Classifier Hard Label

August 23, 2024

Augmented Functional Random Forests: Classifier Construction and Unbiased Functional Principal Components Importance through Ad-Hoc Conditional Permutations
Fabrizio Maturo, Annamaria Porreca
Functional Data Analysis Classification Tree Permutation Importance Learning Classifier Functional Principal Component

November 29, 2023

A Probabilistic Method to Predict Classifier Accuracy on Larger Datasets given Small Pilot Data
Ethan Harvey, Wansu Chen, David M. Kent, Michael C. Hughes
Gaussian Process Classification Performance Pilot Study Large Datasets Probabilistic Approach Trend Extrapolation Learning Classifier

May 17, 2023

Pittsburgh Learning Classifier Systems for Explainable Reinforcement Learning: Comparing with XCS
Jordan T. Bishop, Marcus Gallagher, Will N. Browne
Reinforcement Learning Explainable AI Explainable Reinforcement Learning Learning Classifier Evolutionary Machine Learning

December 16, 2022

Learning Classifiers of Prototypes and Reciprocal Points for Universal Domain Adaptation
Sungsu Hur, Inkyu Shin, Kwanyong Park, Sanghyun Woo, In So Kweon
Domain Shift Visual Prototype Universal Domain Adaptation Learning Classifier

November 28, 2022

Malign Overfitting: Interpolation Can Provably Preclude Invariance
Yoav Wald, Gal Yona, Uri Shalit, Yair Carmon
Distribution Generalization Model Overfitting Interpolation Regime Alpha Invariance Benign Overfitting Non Parametric Learning Classifier Invariance Regularization

November 10, 2022

Review of Methods for Handling Class-Imbalanced in Classification Problems
Satyendra Singh Rawat, Amit Kumar Mishra
Narrative Review NCD Method Class Imbalance Classification Problem Classification Application Improved Training Learning Classifier

October 22, 2022

Learning Classifiers for Imbalanced and Overlapping Data
Shivaditya Shivganesh, Nitin Narayanan N, Pranav Murali, Ajaykumar M
Decision Tree Class Imbalance Simple Classifier Group Imbalance Classification Rule Learning Classifier

July 12, 2022

Investigating the Impact of Independent Rule Fitnesses in a Learning Classifier System
Michael Heider, Helena Stegherr, Jonathan Wurth, Roman Sraj, Jörg Hähner
Global Impact High Explainability Rule Based Fitness Function Learning Classifier Rule Discovery

July 1, 2022

Learning Classifier Systems for Self-Explaining Socio-Technical-Systems
Michael Heider, Helena Stegherr, Richard Nordsieck, Jörg Hähner
Decision Support System Rule Based Socio Technical System Learning Classifier

April 14, 2022

CLUES: A Benchmark for Learning Classifiers using Natural Language Explanations
Rakesh R Menon, Sayan Ghosh, Shashank Srivastava
New Benchmark Supervised Learning Natural Language Explanation Language Supervision Superficial Clue Inductive Learning Learning Classifier

March 17, 2022

Inducing Neural Collapse in Imbalanced Learning: Do We Really Need a Learnable Classifier at the End of Deep Neural Network?
Yibo Yang, Shixiang Chen, Xiangtai Li, Liang Xie, Zhouchen Lin, Dacheng Tao
Deep Neural Network Neural Collapse Linear Classifier Annotated End State Imbalanced Learning Learning Classifier Frame Classification

March 13, 2022

Adaptive Model Predictive Control by Learning Classifiers
Rel Guzman, Rafael Oliveira, Fabio Ramos
Predictive Control Model Predictive Control Bayesian Optimisation Robust Controller Learning Classifier Hybrid MPC Predictive Path

February 3, 2022

Separating Rule Discovery and Global Solution Composition in a Learning Classifier System
Michael Heider, Helena Stegherr, Jonathan Wurth, Roman Sraj, Jörg Hähner
Line by Line Explanation High Explainability Rule Based Fitness Function Learning Classifier Rule Discovery Global Solution

December 8, 2021

CoSSL: Co-Learning of Representation and Classifier for Imbalanced Semi-Supervised Learning
Yue Fan, Dengxin Dai, Anna Kukleva, Bernt Schiele
Representation Learning Simple Classifier Individual Representation Data Imbalance Test Set Learning Classifier Imbalanced Semi Supervised Learning

Learning Classifier

Papers

Improving Online Bagging for Complex Imbalanced Data Stream

Learning with Confidence: Training Better Classifiers from Soft Labels

Augmented Functional Random Forests: Classifier Construction and Unbiased Functional Principal Components Importance through Ad-Hoc Conditional Permutations

A Probabilistic Method to Predict Classifier Accuracy on Larger Datasets given Small Pilot Data

Pittsburgh Learning Classifier Systems for Explainable Reinforcement Learning: Comparing with XCS

Learning Classifiers of Prototypes and Reciprocal Points for Universal Domain Adaptation

Malign Overfitting: Interpolation Can Provably Preclude Invariance

Review of Methods for Handling Class-Imbalanced in Classification Problems

Learning Classifiers for Imbalanced and Overlapping Data

Investigating the Impact of Independent Rule Fitnesses in a Learning Classifier System

Learning Classifier Systems for Self-Explaining Socio-Technical-Systems

CLUES: A Benchmark for Learning Classifiers using Natural Language Explanations

Inducing Neural Collapse in Imbalanced Learning: Do We Really Need a Learnable Classifier at the End of Deep Neural Network?

Adaptive Model Predictive Control by Learning Classifiers

Separating Rule Discovery and Global Solution Composition in a Learning Classifier System

CoSSL: Co-Learning of Representation and Classifier for Imbalanced Semi-Supervised Learning