Traditional Machine Learning

Traditional machine learning (ML) focuses on developing algorithms that learn patterns from data to make predictions or decisions, aiming for accuracy, efficiency, and interpretability. Current research emphasizes improving model robustness and interpretability, particularly in applications with limited data, exploring algorithms like support vector machines, random forests, and Bayesian networks, often within ensemble methods. This field remains crucial for various applications, from healthcare and finance to environmental monitoring, providing valuable tools for data analysis and decision-making where deep learning's complexity or data requirements are prohibitive. The ongoing exploration of traditional ML's strengths, particularly in interpretability and resource efficiency, continues to yield significant advancements.

Papers

November 10, 2024

Fitting Multiple Machine Learning Models with Performance Based Clustering
Mehmet Efe Lorasdagi, Ahmet Berker Koc, Ali Taha Koc, Suleyman Serdar Kozat
Machine Learning Model System Performance Traditional Machine Learning Weighted Ensemble Clustering Framework

October 28, 2024

Towards Trustworthy Machine Learning in Production: An Overview of the Robustness in MLOps Approach
Firas Bayram, Bestoun S. Ahmed
Machine Learning Native Robustness Traditional Machine Learning Production Incident Trustworthy Machine Learning MLOps System

October 26, 2024

Transforming Precision: A Comparative Analysis of Vision Transformers, CNNs, and Traditional ML for Knee Osteoarthritis Severity Diagnosis
Tasnim Sakib Apon, Md.Fahim-Ul-Islam, Nafiz Imtiaz Rafin, Joya Akter, Md. Golam Rabiul Alam
Vision Transformer CNN Model Image Processing Multidimensional Local Precision Rate Traditional Machine Learning ViT Model Knee Osteoarthritis Knee Osteoarthritis Severity

September 23, 2024

CauSkelNet: Causal Representation Learning for Human Behaviour Analysis
Xingrui Gu, Chuyi Jiang, Erte Wang, Zekun Wu, Qiang Cui, Leimin Tian, Lianlong Wu, Siyang Song, Chuang Yu
Human Motion Causal Representation Learning Traditional Machine Learning Human Motion Analysis GCN Model

August 3, 2024

Classical Machine Learning: Seventy Years of Algorithmic Learning Evolution
Absalom E. Ezugwu, Yuh-Shan Ho, Ojonukpe S. Egwuche, Olufisayo S. Ekundayo, Annette Van Der Merwe, Apu K. Saha, Jayanta Pal
Machine Learning Last Decade Evolution Strategy Traditional Machine Learning Classical Machine Learning

July 25, 2024

StraightLine: An End-to-End Resource-Aware Scheduler for Machine Learning Application Requests
Cheng-Wei Ching, Boyuan Guan, Hailu Xu, Liting Hu
Machine Learning End to End Machine Learning System Machine Learning Application Traditional Machine Learning Aware Job

July 18, 2024

Evaluating Large Language Models for Anxiety and Depression Classification using Counseling and Psychotherapy Transcripts
Junwei Sun, Siqi Ma, Yiran Fan, Peter Washington
Large Language Model Long Span Traditional Machine Learning Depression Recognition Psychotherapy Session Anxiety Disorder

July 4, 2024

Meta-Learning and representation learner: A short theoretical note
Mouad El Bouchattaoui
Meta Learning Theoretical Analysis Traditional Machine Learning Learning Process Representation Learner

May 26, 2024

Predicting Rental Price of Lane Houses in Shanghai with Machine Learning Methods and Large Language Models
Tingting Chen, Shijing Si
Machine Learning Machine Learning Model Ridge Regression Rural China Traditional Machine Learning Airbnb Rental Lane Instance

April 3, 2024

Learning Alternative Ways of Performing a Task
David Nieves, María José Ramírez-Quintana, Carlos Monserrat, César Ferri, José Hernández-Orallo
Related Task Traditional Machine Learning Learning Method Inductive Learning Human Activity Recognition Activity Sequence Surgical Training

March 17, 2024

Cheap Ways of Extracting Clinical Markers from Texts
Anastasia Sandu, Teodor Mihailescu, Sergiu Nisioi
Chain of Thought Evidence Piece Text Based Tf Idf Traditional Machine Learning

March 7, 2024

Comparison of gait phase detection using traditional machine learning and deep learning techniques
Farhad Nazari, Navid Mohajer, Darius Nahavandi, Abbas Khosravi
Deep Learning Consistent Comparison Surface Electromyography Training Deep Traditional Machine Learning Human Gait Gait Phase Electromyography Data

March 3, 2024

Machine Learning vs Deep Learning: The Generalization Problem
Yong Yi Bay, Kathleen A. Yearick
Deep Learning Machine Learning Deep Learning Model Strong Generalization Extrapolation Framework Traditional Machine Learning Generalization Problem

February 15, 2024

Utilizing Deep Learning for Enhancing Network Resilience in Finance
Yulu Gong, Mengran Zhu, Shuning Huo, Yafei Xiang, Hanyi Yu
Network Intrusion Detection Traditional Machine Learning Threat Detection Network Resilience Based Intrusion Detection

February 4, 2024

Surfing the modeling of PoS taggers in low-resource scenarios
Manuel Vilares Ferro, Víctor M. Darriba Bilbao, Francisco J. Ribadas-Pena, Jorge Graña Gil
Natural Language Processing Model Selection Traditional Machine Learning Po Tagger Low Resource Scenario

October 23, 2023

Confronting LLMs with Traditional ML: Rethinking the Fairness of Large Language Models in Tabular Classifications
Yanchen Liu, Srishti Gautam, Jiaqi Ma, Himabindu Lakkaraju
Large Language Model Procedural Fairness Bias Mitigation Social Bias Traditional Machine Learning Tabular Data Classification

October 9, 2023

Ensemble-based Hybrid Optimization of Bayesian Neural Networks and Traditional Machine Learning Algorithms
Peiwen Tan
Bayesian Neural Network Hyperparameter Tuning Ensemble Method Gradient Boosting Traditional Machine Learning Hybrid Optimization

August 28, 2023

Comparing AutoML and Deep Learning Methods for Condition Monitoring using Realistic Validation Scenarios
Payman Goodarzi, Andreas Schütze, Tizian Schneider
Deep Learning Online autoML Cross Validation Traditional Machine Learning Condition Monitoring

August 1, 2023

GRDD: A Dataset for Greek Dialectal NLP
Stergios Chatzikyriakidis, Chatrine Qwaider, Ilias Kolokousis, Christina Koula, Dimitris Papadakis, Efthymia Sakellariou
Data Set Machine Learning Model Traditional Machine Learning Dialect Datasets Dialect Identification Relation Distillation

July 31, 2023

A Comprehensive Study of Machine Learning Techniques for Log-Based Anomaly Detection
Shan Ali, Chaima Boufaied, Domenico Bianculli, Paula Branco, Lionel Briand
Deep Learning Machine Learning Comprehensive Study Traditional Machine Learning Log Based Anomaly Detection Log Analysis Task

Traditional Machine Learning

Papers

Fitting Multiple Machine Learning Models with Performance Based Clustering

Towards Trustworthy Machine Learning in Production: An Overview of the Robustness in MLOps Approach

Transforming Precision: A Comparative Analysis of Vision Transformers, CNNs, and Traditional ML for Knee Osteoarthritis Severity Diagnosis

CauSkelNet: Causal Representation Learning for Human Behaviour Analysis

Classical Machine Learning: Seventy Years of Algorithmic Learning Evolution

StraightLine: An End-to-End Resource-Aware Scheduler for Machine Learning Application Requests

Evaluating Large Language Models for Anxiety and Depression Classification using Counseling and Psychotherapy Transcripts

Meta-Learning and representation learner: A short theoretical note

Predicting Rental Price of Lane Houses in Shanghai with Machine Learning Methods and Large Language Models

Learning Alternative Ways of Performing a Task

Cheap Ways of Extracting Clinical Markers from Texts

Comparison of gait phase detection using traditional machine learning and deep learning techniques

Machine Learning vs Deep Learning: The Generalization Problem

Utilizing Deep Learning for Enhancing Network Resilience in Finance

Surfing the modeling of PoS taggers in low-resource scenarios

Confronting LLMs with Traditional ML: Rethinking the Fairness of Large Language Models in Tabular Classifications

Ensemble-based Hybrid Optimization of Bayesian Neural Networks and Traditional Machine Learning Algorithms

Comparing AutoML and Deep Learning Methods for Condition Monitoring using Realistic Validation Scenarios

GRDD: A Dataset for Greek Dialectal NLP

A Comprehensive Study of Machine Learning Techniques for Log-Based Anomaly Detection