mi-DS: Multiple-Instance Learning Algorithm

Multiple-instance learning (MIL) is a supervised learning technique that addresses the problem of classifying bags of instances instead of single instances. In this paper, we introduce a rule-based MIL algorithm, called mi-DS, and compare it with 21 existing MIL algorithms on 26 commonly used data s...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:IEEE transactions on cybernetics Ročník 43; číslo 1; s. 143 - 154
Hlavní autoři: Nguyen, Dat T., Nguyen, Cao D., Hargraves, Rosalyn, Kurgan, Lukasz A., Cios, Krzysztof J.
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.02.2013
Témata:
Algorithms
Animals
Classification algorithms
Computational Biology
Databases, Factual
Educational institutions
Machine Learning
Models, Statistical
Multiple-instance learning (MIL)
Prediction algorithms
Proteins
rule-based algorithms
supervised learning
Support vector machines
Training data
ISSN:2168-2267, 2168-2275, 2168-2275
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Multiple-instance learning (MIL) is a supervised learning technique that addresses the problem of classifying bags of instances instead of single instances. In this paper, we introduce a rule-based MIL algorithm, called mi-DS, and compare it with 21 existing MIL algorithms on 26 commonly used data sets. The results show that mi-DS performs on par with or better than several well-known algorithms and generates models characterized by balanced values of precision and recall. Importantly, the introduced method provides a framework that can be used for converting other rule-based algorithms into MIL algorithms.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TSMCB.2012.2201468