Instance Selection

Instance selection focuses on optimizing machine learning model training by carefully choosing a subset of the available data, aiming to improve model performance, efficiency, and generalization. Current research explores instance selection within various contexts, including reinforcement learning for dynamic algorithm configuration, improving the robustness of interactive segmentation models for medical image analysis, and mitigating data sparsity in electronic health records. These advancements have significant implications for diverse fields, ranging from medical diagnostics and personalized medicine to efficient algorithm design and anomaly detection in large datasets.

Papers

July 14, 2022

Instance Selection Mechanisms for Human-in-the-Loop Systems in Few-Shot Learning
Johannes Jakubik, Benedikt Blumenstiel, Michael Vössing, Patrick Hemmer
Machine Learning LeArning Abstract Shot Learning Human in the Loop Instance Selection Shot Learning Evaluation

June 28, 2022

Parallel Instance Filtering for Malware Detection
Martin Jureček, Olha Jurečková
Malware Detection Instance Selection

May 16, 2022

PRISM: Pre-trained Indeterminate Speaker Representation Model for Speaker Diarization and Speaker Verification
Siqi Zheng, Hongbin Suo, Qian Chen
Speaker Verification Speaker Diarization Speaker Embeddings Speaker Representation Instance Selection

May 12, 2022

Feature and Instance Joint Selection: A Reinforcement Learning Perspective
Wei Fan, Kunpeng Liu, Hao Liu, Hengshu Zhu, Hui Xiong, Yanjie Fu
Efficient Exploration Content Based Feature Reinforcement Learning Approach Instance Selection Joint Selection Reinforcement Learning Solution

April 20, 2022

Interventional Multi-Instance Learning with Deconfounded Instance-Level Prediction
Tiancheng Lin, Hongteng Xu, Canqian Yang, Yi Xu
Instance Learning Causal Intervention Instance Selection

April 4, 2022

Event Log Sampling for Predictive Monitoring
Mohammadreza Fani Sani, Mozhgan Vazifehdoostirani, Gyunam Park, Marco Pegoraro, Sebastiaan J. van Zelst, Wil M. P. van der Aalst
Process Mining Instance Selection Predictive Process Monitoring Training Instance Predictive Monitoring

November 16, 2021

Outlier Detection as Instance Selection Method for Feature Selection in Time Series Classification
David Cemernek
Feature Selection Time Series Classification Outlier Detection Better Interpretability Instance Selection Heavy Undocumented Preprocessing

Instance Selection

Papers

Instance Selection Mechanisms for Human-in-the-Loop Systems in Few-Shot Learning

Parallel Instance Filtering for Malware Detection

PRISM: Pre-trained Indeterminate Speaker Representation Model for Speaker Diarization and Speaker Verification

Feature and Instance Joint Selection: A Reinforcement Learning Perspective

Interventional Multi-Instance Learning with Deconfounded Instance-Level Prediction

Event Log Sampling for Predictive Monitoring

Outlier Detection as Instance Selection Method for Feature Selection in Time Series Classification