Subgroup discovery in non‐inferiority trials

Classification rule learning using subgroup discovery of cross-domain attributes responsible for design-silicon mismatch

Proceedings of the 47th Design Automation Conference on - DAC '10 ◽

10.1145/1837274.1837368 ◽

2010 ◽

Cited By ~ 9

Author(s):

Nicholas Callegari ◽

Dragoljub (Gagi) Drmanac ◽

Li-C. Wang ◽

Magdy S. Abadir

Keyword(s):

Rule Learning ◽

Classification Rule ◽

Subgroup Discovery ◽

Cross Domain

Anytime Subgroup Discovery in Numerical Domains with Guarantees

Machine Learning and Knowledge Discovery in Databases - Lecture Notes in Computer Science ◽

10.1007/978-3-030-10928-8_30 ◽

2019 ◽

pp. 500-516 ◽

Cited By ~ 5

Author(s):

Aimene Belfodil ◽

Adnene Belfodil ◽

Mehdi Kaytoue

Keyword(s):

Subgroup Discovery

ADOPS: Aspect Discovery OPinion Summarisation Methodology based on deep learning and subgroup discovery for generating explainable opinion summaries

Knowledge-Based Systems ◽

10.1016/j.knosys.2021.107455 ◽

2021 ◽

pp. 107455

Author(s):

Miguel López ◽

Eugenio Martínez-Cámara ◽

M. Victoria Luzón ◽

Francisco Herrera

Keyword(s):

Deep Learning ◽

Subgroup Discovery

Distinct developmental profiles in typical speech acquisition

Journal of Neurophysiology ◽

10.1152/jn.00337.2010 ◽

2012 ◽

Vol 107 (10) ◽

pp. 2885-2900 ◽

Cited By ~ 11

Author(s):

Jennell C. Vick ◽

Thomas F. Campbell ◽

Lawrence D. Shriberg ◽

Jordan R. Green ◽

Hervé Abdi ◽

...

Keyword(s):

Speech Development ◽

Developmental Pathways ◽

Subgroup Discovery ◽

Typically Developing ◽

Speech Acquisition ◽

Typically Developing Children ◽

Lower Lip ◽

Syllable Duration ◽

High Level ◽

Syllable Stress

Three- to five-year-old children produce speech that is characterized by a high level of variability within and across individuals. This variability, which is manifest in speech movements, acoustics, and overt behaviors, can be input to subgroup discovery methods to identify cohesive subgroups of speakers or to reveal distinct developmental pathways or profiles. This investigation characterized three distinct groups of typically developing children and provided normative benchmarks for speech development. These speech development profiles, identified among 63 typically developing preschool-aged speakers (ages 36–59 mo), were derived from the children's performance on multiple measures. These profiles were obtained by submitting to a k-means cluster analysis of 72 measures that composed three levels of speech analysis: behavioral (e.g., task accuracy, percentage of consonants correct), acoustic (e.g., syllable duration, syllable stress), and kinematic (e.g., variability of movements of the upper lip, lower lip, and jaw). Two of the discovered group profiles were distinguished by measures of variability but not by phonemic accuracy; the third group of children was characterized by their relatively low phonemic accuracy but not by an increase in measures of variability. Analyses revealed that of the original 72 measures, 8 key measures were sufficient to best distinguish the 3 profile groups.

RSD: Relational Subgroup Discovery through First-Order Feature Construction

Inductive Logic Programming - Lecture Notes in Computer Science ◽

10.1007/3-540-36468-4_10 ◽

2003 ◽

pp. 149-165 ◽

Cited By ~ 17

Author(s):

Nada Lavrač ◽

Filip Železný ◽

Peter A. Flach

Keyword(s):

Subgroup Discovery ◽

Feature Construction ◽

First Order