Papers Subgroup Discovery
“Subgroup Discovery” 태그가 달린 논문 47편 · 필터 해제
Discovering multiple antibiotic resistance phenotypes using diverse top-k subgroup list discovery
Antibiotic resistance is one of the major global threats to human health and occurs when antibiotics lose their ability to combat bacterial infections. In this problem, a clinical decision support system could use phenot…
Data MiningDecision MakingDiverse Top-k Subgroup List DiscoveryPatient Phenotyping+1Subgroup Performance Analysis in Hidden Stratifications
Machine learning (ML) models may suffer from significant performance disparities between patient groups. Identifying such disparities by monitoring performance at a granular level is crucial for safely deploying ML to ea…
Lesion ClassificationSkin Lesion ClassificationSubgroup DiscoveryA consensus set for the aggregation of partial rankings: the case of the Optimal Set of Bucket Orders Problem
In rank aggregation problems (RAP), the solution is usually a consensus ranking that generalizes a set of input orderings. There are different variants that differ not only in terms of the type of rankings that are used …
Subgroup DiscoverySubgroups: A Python library for Subgroup Discovery
This manuscript introduces Subgroups, an openly accessible Python library designed to ease the use of Subgroup Discovery (SD) algorithms for machine learning and data science. The Subgroups Library offers several advanta…
Data MiningSubgroup DiscoveryFairpriori: Improving Biased Subgroup Discovery for Deep Neural Network Fairness
While deep learning has become a core functional module of most software systems, concerns regarding the fairness of ML predictions have emerged as a significant issue that affects prediction results due to discriminatio…
FairnessSubgroup DiscoveryUsing Constraints to Discover Sparse and Alternative Subgroup Descriptions
Subgroup-discovery methods allow users to obtain simple descriptions of interesting regions in a dataset. Using constraints in subgroup discovery can enhance interpretability even further. In this article, we focus on tw…
Binary ClassificationHeuristic SearchSubgroup DiscoveryPeripheral Nervous System Responses to Food Stimuli: Analysis Using Data Science Approaches
In the field of food, as in other fields, the measurement of emotional responses to food and their sensory properties is a major challenge. In the present protocol, we propose a step-by-step procedure that allows a physi…
DescriptiveSubgroup DiscoveryDiscover and Mitigate Multiple Biased Subgroups in Image Classifiers
Machine learning models can perform well on in-distribution data but often fail on biased subgroups that are underrepresented in the training data, hindering the robustness of models for reliable applications. Such subgr…
Dimensionality ReductionSubgroup DiscoverySubgroup Discovery in MOOCs: A Big Data Application for Describing Different Types of Learners
The aim of this paper is to categorize and describe different types of learners in massive open online courses (MOOCs) by means of a subgroup discovery approach based on MapReduce. The final objective is to discover IF-T…
DescriptiveSubgroup DiscoveryFairCompass: Operationalising Fairness in Machine Learning
As artificial intelligence (AI) increasingly becomes an integral part of our societal and individual activities, there is a growing imperative to develop responsible AI solutions. Despite a diverse assortment of machine …
FairnessSubgroup DiscoveryData is Moody: Discovering Data Modification Rules from Process Event Logs
Although event logs are a powerful source to gain insight about the behavior of the underlying business process, existing work primarily focuses on finding patterns in the activity sequences of an event log, while ignori…
AttributeSubgroup DiscoveryMining Java Memory Errors using Subjective Interesting Subgroups with Hierarchical Targets
Software applications, especially Enterprise Resource Planning (ERP) systems, are crucial to the day-to-day operations of many industries. Therefore, it is essential to maintain these systems effectively using tools that…
ERPSubgroup DiscoveryA new algorithm for Subgroup Set Discovery based on Information Gain
Pattern discovery is a machine learning technique that aims to find sets of items, subsequences, or substructures that are present in a dataset with a higher frequency value than a manually set threshold. This process he…
General KnowledgeSubgroup DiscoveryVLSD—An Efficient Subgroup Discovery Algorithm Based on Equivalence Classes and Optimistic Estimate
Subgroup Discovery (SD) is a supervised data mining technique for identifying a set of relations (subgroups) among attributes from a dataset with respect to a target attribute. Two key components of this technique are (i…
AttributeData MiningSubgroup DiscoveryTree-based Subgroup Discovery In Electronic Health Records: Heterogeneity of Treatment Effects for DTG-containing Therapies
The rich longitudinal individual level data available from electronic health records (EHRs) can be used to examine treatment effect heterogeneity. However, estimating treatment effects using EHR data poses several challe…
Subgroup DiscoverySubgroup Discovery in Unstructured Data
Subgroup discovery is a descriptive and exploratory data mining technique to identify subgroups in a population that exhibit interesting behavior with respect to a variable of interest. Subgroup discovery has numerous ap…
AttributeDescriptiveSubgroup DiscoveryAccelerating System-Level Debug Using Rule Learning and Subgroup Discovery Techniques
We propose a root-causing procedure for accelerating system-level debug using rule-based techniques. We describe the procedure and how it provides high quality debug hints for reducing the debug effort. This includes the…
ManagementSubgroup DiscoverySubgroup discovery of Parkinson's Disease by utilizing a multi-modal smart device system
In recent years, sensors from smart consumer devices have shown great diagnostic potential in movement disorders. In this context, data modalities such as electronic questionnaires, hand movement and voice captures have …
DiagnosticSubgroup DiscoverySparsity-based Feature Selection for Anomalous Subgroup Discovery
Anomalous pattern detection aims to identify instances where deviation from normalcy is evident, and is widely applicable across domains. Multiple anomalous detection techniques have been proposed in the state of the art…
feature selectionSubgroup DiscoveryPedagogical Rule Extraction to Learn Interpretable Models - an Empirical Study
Machine-learning models are ubiquitous. In some domains, for instance, in medicine, the models' predictions must be interpretable. Decision trees, classification rules, and subgroup discovery are three broad categories o…
BIG-bench Machine LearningSubgroup Discovery