Orthopartitions in Knowledge Representation and Machine Learning

被引：1

作者：

Ciucci, Davide ^{[1
]}

Boffa, Stefania ^{[1
]}

Campagner, Andrea ^{[1
]}

机构：

[1] Univ Milano Bicocca, DISCo, Viale Sarca 336, I-20126 Milan, Italy

来源：

ROUGH SETS, IJCRS 2022 | 2022年 / 13633卷

关键词：

Credal partition; Decision tree; Rough sets; Intuitionistic fuzzy sets; Orthopairs; Partial labels; Partition; Possibility theory; Soft clustering; ORTHOPAIRS; 3-WAY;

D O I：

10.1007/978-3-031-21244-4_1

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Orthopartitions are partitions with uncertainty. We survey their use in knowledge representation (KR) and machine learning (ML). In particular, in KR their connection with possibility theory, intuitionistic fuzzy sets and credal partitions is discussed. As far as ML is concerned, their use in soft clustering evaluation and to define generalized decision trees are recalled. The (open) problem of relating an orthopartition to a partial equivalence relation is also sketched.

引用

页码：3 / 18

页数：16

共 50 条

[1] Towards hybridization of knowledge representation and machine learning
Khorsi, Ahmed
[J]. COMPUTING AND INFORMATICS, 2007, 26 (02) : 123 - 147
[2] A Unified Model Representation of Machine Learning Knowledge
Enriquez, J. G.
Martinez-Rojas, A.
Lizcano, D.
Jimenez-Ramirez, A.
[J]. JOURNAL OF WEB ENGINEERING, 2020, 19 (02): : 319 - 340
[3] Towards a Unified Model Representation of Machine Learning Knowledge
Martinez-Rojas, Antonio
Jimenez-Ramirez, Andres
Enriquez, Jose G.
[J]. WEBIST: PROCEEDINGS OF THE 15TH INTERNATIONAL CONFERENCE ON WEB INFORMATION SYSTEMS AND TECHNOLOGIES, 2019, : 470 - 476
[4] KNOWLEDGE REPRESENTATION AND ORGANIZATION IN MACHINE LEARNING - SIEKMANN,MK
KRAUSE, B
[J]. ZEITSCHRIFT FUR PSYCHOLOGIE, 1991, 199 (02): : 205 - 205
[5] Machine learning and knowledge representation in the LaboUr approach to user modeling
Pohl, W
Nick, A
[J]. UM99: USER MODELING, PROCEEDINGS, 1999, (407): : 179 - 188
[6] KNOWLEDGE REPRESENTATION ON THE CONNECTION MACHINE
EVETT, M
SPECTOR, L
HENDLER, J
[J]. PROCEEDINGS : SUPERCOMPUTING 89, 1989, : 283 - 293
[7] KNOWLEDGE REPRESENTATION AND MACHINE TRANSLATION
SAWAI, S
SUGIMOTO, M
UKAI, N
[J]. FUJITSU SCIENTIFIC & TECHNICAL JOURNAL, 1982, 18 (01): : 117 - 133
[8] KNOWLEDGE REPRESENTATION MODEL WHICH COMBINES CONCEPTUAL GRAPHS AND FUZZINESS FOR MACHINE LEARNING
BARON, E
ORBACH, R
AMIT, G
[J]. LECTURE NOTES IN COMPUTER SCIENCE, 1987, 286 : 307 - 318
[9] Knowledge representation and machine learning on wearable sensor data: a study on gait monitoring
Lopez, Beatriz
Mordvanyuk, Natalia
Gay, Pablo
Pla, Albert
[J]. PROCEEDINGS OF THE FIRST INTERNATIONAL CONFERENCE ON DATA SCIENCE, E-LEARNING AND INFORMATION SYSTEMS 2018 (DATA'18), 2018,
[10] From Shallow to Deep Interactions Between Knowledge Representation, Reasoning and Machine Learning
Amel, Kay R.
[J]. SCALABLE UNCERTAINTY MANAGEMENT, SUM 2019, 2019, 11940 : 447 - 448

← 1 2 3 4 5 →