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標(biāo)題: Titlebook: Dealing with Imbalanced and Weakly Labelled Data in Machine Learning using Fuzzy and Rough Set Metho; Sarah Vluymans Book 2019 Springer Na [打印本頁]

作者: 熱情美女    時(shí)間: 2025-3-21 19:03
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作者: 男生如果明白    時(shí)間: 2025-3-21 21:16

作者: grotto    時(shí)間: 2025-3-22 00:30
Udoka Okonta,Amin Hosseinian-Farphenomenon is addressed by the introduction of semi-supervised classification, in which a prediction model is derived from a training set consisting of both labelled and unlabelled data. Information in both the labelled and unlabelled parts of the training set can be used in the classification process.
作者: 拾落穗    時(shí)間: 2025-3-22 06:48

作者: 沙發(fā)    時(shí)間: 2025-3-22 12:47
CSR, Sustainability, Ethics & Governancequence, the recognition of minority instances is hampered. Since minority classes are usually the ones of interest, custom techniques are required to deal with such data skewness. We study them in this chapter.
作者: CHAR    時(shí)間: 2025-3-22 15:10

作者: CHAR    時(shí)間: 2025-3-22 18:15
Matthew D. Wood,Daniel A. HunterGenerally put, this book is on fuzzy rough set based methods for machine learning. We develop classification algorithms based on fuzzy rough set theory for several types of data relevant to real-world applications.
作者: 陪審團(tuán)每個(gè)人    時(shí)間: 2025-3-22 21:42

作者: Dawdle    時(shí)間: 2025-3-23 03:45
Matthew D. Wood,Daniel A. HunterAs noted in Chap.?1, the traditional fuzzy rough set model is intrinsically sensitive to noise and outliers in the data. One generalization to deal with this issue in an intuitive way is the ordered weighted average (OWA) based fuzzy rough set model, that replaces the strict minimum and maximum operators by more elaborate OWA aggregations.
作者: 泥瓦匠    時(shí)間: 2025-3-23 09:27

作者: condescend    時(shí)間: 2025-3-23 11:49
https://doi.org/10.1007/978-3-031-58878-5The defining characteristic of multi-label as opposed to single-label data is that each instance can belong to several classes at once. The multi-label classification task is to predict all relevant labels of a target instance. This chapter presents and experimentally evaluates our FRONEC method, the Fuzzy Rough NEighbourhood Consensus.
作者: 手術(shù)刀    時(shí)間: 2025-3-23 17:13
Introduction,Generally put, this book is on fuzzy rough set based methods for machine learning. We develop classification algorithms based on fuzzy rough set theory for several types of data relevant to real-world applications.
作者: 智力高    時(shí)間: 2025-3-23 19:28
Classification,In this chapter, we review the traditional classification domain, the supervised learning task on which this book focuses. Before addressing several challenging classification problems in the next chapters, we first review the core aspects of this popular research area, as would be done in any machine learning course or handbook.
作者: Nebulizer    時(shí)間: 2025-3-23 22:56
Understanding OWA Based Fuzzy Rough Sets,As noted in Chap.?1, the traditional fuzzy rough set model is intrinsically sensitive to noise and outliers in the data. One generalization to deal with this issue in an intuitive way is the ordered weighted average (OWA) based fuzzy rough set model, that replaces the strict minimum and maximum operators by more elaborate OWA aggregations.
作者: Amenable    時(shí)間: 2025-3-24 05:16
Multi-instance Learning,The domain of multi-instance learning (MIL) deals with datasets consisting of compound data samples. Instead of representing an observation as an instance described by a single feature vector, each observation (called a bag) corresponds to a set of instances and, consequently, a set of feature vectors.
作者: Jargon    時(shí)間: 2025-3-24 10:23
Multi-label Learning,The defining characteristic of multi-label as opposed to single-label data is that each instance can belong to several classes at once. The multi-label classification task is to predict all relevant labels of a target instance. This chapter presents and experimentally evaluates our FRONEC method, the Fuzzy Rough NEighbourhood Consensus.
作者: gene-therapy    時(shí)間: 2025-3-24 12:50
Sarah VluymansTakes the research on ordered weighted average (OWA) fuzzy rough sets to the next level.Provides clear guidelines on how to use them.Expands the application to e.g. imbalanced, semi-supervised, multi-
作者: 推測    時(shí)間: 2025-3-24 17:37
Studies in Computational Intelligencehttp://image.papertrans.cn/d/image/263975.jpg
作者: 屈尊    時(shí)間: 2025-3-24 21:40

作者: FECT    時(shí)間: 2025-3-25 00:49
CSR, Sustainability, Ethics & Governanceibution of observations among them, the classification task is inherently more challenging. Traditional classification algorithms (see Sect.?.) tend to favour majority over minority class elements due to their incorrect implicit assumption of an equal class representation during learning. As a conse
作者: 得意牛    時(shí)間: 2025-3-25 07:08

作者: Alpha-Cells    時(shí)間: 2025-3-25 09:09
Professional and Practice-based Learningata, semi-supervised data, multi-instance data and multi-label data. Fuzzy rough set theory allows to model the uncertainty present in data both in terms of vagueness (fuzziness) and indiscernibility or imprecision (roughness).
作者: 臥虎藏龍    時(shí)間: 2025-3-25 11:51
https://doi.org/10.1007/978-3-030-04663-7Computational Intelligence; OWA; Ordered Weighted Average; Classification; Multi-Instance Learning; Multi
作者: 地名詞典    時(shí)間: 2025-3-25 18:59
Springer Nature Switzerland AG 2019
作者: 玩笑    時(shí)間: 2025-3-25 21:26

作者: CHART    時(shí)間: 2025-3-26 03:46
Professional and Practice-based Learningata, semi-supervised data, multi-instance data and multi-label data. Fuzzy rough set theory allows to model the uncertainty present in data both in terms of vagueness (fuzziness) and indiscernibility or imprecision (roughness).
作者: Femish    時(shí)間: 2025-3-26 05:15
Learning from Imbalanced Data,ibution of observations among them, the classification task is inherently more challenging. Traditional classification algorithms (see Sect.?.) tend to favour majority over minority class elements due to their incorrect implicit assumption of an equal class representation during learning. As a conse
作者: 侵害    時(shí)間: 2025-3-26 11:03

作者: neoplasm    時(shí)間: 2025-3-26 14:23
Conclusions and Future Work,ata, semi-supervised data, multi-instance data and multi-label data. Fuzzy rough set theory allows to model the uncertainty present in data both in terms of vagueness (fuzziness) and indiscernibility or imprecision (roughness).
作者: 時(shí)代錯(cuò)誤    時(shí)間: 2025-3-26 16:48

作者: conjunctiva    時(shí)間: 2025-3-26 21:36

作者: Sinus-Node    時(shí)間: 2025-3-27 01:37
1860-949X hematicians, computer scientists and engineers, the content will also be interesting and accessible to students and professionals from a range of other fields.? ?.978-3-030-04663-7Series ISSN 1860-949X Series E-ISSN 1860-9503
作者: 連鎖    時(shí)間: 2025-3-27 08:34

作者: 尾巴    時(shí)間: 2025-3-27 11:08

作者: 領(lǐng)導(dǎo)權(quán)    時(shí)間: 2025-3-27 14:10
Day Centres,ot reliant upon it for either accommodation or funding. Throughout the chapter my aim will be to give an overall impression of day centres, with particular focus on the work, and type of centre with which I am involved.
作者: circumvent    時(shí)間: 2025-3-27 18:11
to avoid this additional devices and surgical or percutaneous draining cannulas need to be placed for unloading of the LV. In this review, current indications for LV unloading are summarized and state-of-the-art unloading strategies and techniques are highlighted.
作者: 織物    時(shí)間: 2025-3-28 01:13

作者: Incommensurate    時(shí)間: 2025-3-28 04:06

作者: DALLY    時(shí)間: 2025-3-28 08:31
Ina FinkeDa die Betriebsdrehzahl der Turbomaschine festliegt, müssen die Schaufeln so ausgelegt werden, da? keine Eigenfrequenz einer Schaufel mit einem Vielfachen der L?uferdrehzahl zusammen- f?llt; Die Erregerfrequenzen k?nnen das SOfache der Betriebsdrehzahl erreichen, so da? auch die bis dahin reichenden Oberschwi978-3-663-06708-5978-3-663-07621-6
作者: 合適    時(shí)間: 2025-3-28 12:56

作者: Favorable    時(shí)間: 2025-3-28 18:15

作者: 爭吵    時(shí)間: 2025-3-28 22:35

作者: 功多汁水    時(shí)間: 2025-3-29 02:27





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