Titlebook: Dealing with Imbalanced and Weakly Labelled Data in Machine Learning using Fuzzy and Rough Set Metho; Sarah Vluymans Book 2019 Springer Na

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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.

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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.

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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.

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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.

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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.

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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.

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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-

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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