PUBLICATION DATE

2017-08-05

AUTHORS

Fang Chun Jiang

TITLE

Study on a confidence machine learning method based on ensemble learning

ISSUE

N/A

VOLUME

N/A

ISSN (print)

1386-7857

ISSN (electronic)

1573-7543

ABSTRACT

Confidence machine learning method is an important research content in machine learning area and of significant importance to high-risk machine learning application area. In confidence machine learning, design and calculation of confidence level is a difficulty; nevertheless, the algorithm in this paper enables confidence classification even when specific threshold setting is omitted and confidence level calculation for each example and unknown sample is neglected. Based on ensemble learning structure, this algorithm employs twice one-class classifier to classify binary problems, and with reject option being set, confidence learning of binary classification is performed by means of multi-layer ensemble learning. The algorithm has been validated on eight experimental datasets, such as heart disease and diabetes mellitus, and good results have been achieved. The learning method proposed in this paper can make the confidence machine learning easier and more efficient. In the era of big data, the research of pattern recognition algorithm based on small sample is still of great significance.

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29 TRIPLES      26 PREDICATES      30 URIs      17 LITERALS

Subject Predicate Object
1 articles:3a72eb54ae3f9e51a018aae6a674ca79 sg:abstract Abstract Confidence machine learning method is an important research content in machine learning area and of significant importance to high-risk machine learning application area. In confidence machine learning, design and calculation of confidence level is a difficulty; nevertheless, the algorithm in this paper enables confidence classification even when specific threshold setting is omitted and confidence level calculation for each example and unknown sample is neglected. Based on ensemble learning structure, this algorithm employs twice one-class classifier to classify binary problems, and with reject option being set, confidence learning of binary classification is performed by means of multi-layer ensemble learning. The algorithm has been validated on eight experimental datasets, such as heart disease and diabetes mellitus, and good results have been achieved. The learning method proposed in this paper can make the confidence machine learning easier and more efficient. In the era of big data, the research of pattern recognition algorithm based on small sample is still of great significance.
2 sg:articleType OriginalPaper
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5 sg:doi 10.1007/s10586-017-1085-z
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16 sg:issnElectronic 1573-7543
17 sg:issnPrint 1386-7857
18 sg:language English
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20 sg:pageEnd 12
21 sg:pageStart 1
22 sg:publicationDate 2017-08-05
23 sg:publicationYear 2017
24 sg:publicationYearMonth 2017-08
25 sg:scigraphId 3a72eb54ae3f9e51a018aae6a674ca79
26 sg:title Study on a confidence machine learning method based on ensemble learning
27 sg:webpage https://link.springer.com/10.1007/s10586-017-1085-z
28 rdf:type sg:Article
29 rdfs:label Article: Study on a confidence machine learning method based on ensemble learning
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