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COMPARATIVE STUDY OF LEARNING FROM IMBALANCED DATA
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- Contributor: projectwaka
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- Case No: 560517pw
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ABSRACT - [ Total Page(s): 1 ]The automation of most of our activities has led to the continuous production of data that arrive in the form of fast-arriving streams. In a supervised learning setting, instances in these streams are labeled as belonging to a particular class. When the number of classes in the data stream is more than two, such a data stream is referred to as a multi-class data stream. Multi-class imbalanced data stream describes the situation where the instance distribution of the classes is skewed, such that instances of some classes occur more frequently than others. Classes with the frequently occurring i
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CHAPTER ONE - [ Total Page(s): 2 ]1.5 Scope of the studyThe study is restricted to the nature of Imbalanced data, providing comparative study of learning schemes for learning from imbalanced data. The scope of the study in broad terms of other than learning from imbalanced data. Few among them are;Machine Learning algorithmic approach to learning from imbalanced data such as decision Trees (The Naïve Bayes Tree), and Artificial Neural network (The Multilayer Perceptron ), Machine learning performance evaluation measures, Performance and monitoring measures used in evaluating imbalanced data learning, Model Creation that would ... Continue Reading
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ABSRACT - [ Total Page(s): 1 ]The automation of most of our activities has led to the continuous production of data that arrive in the form of fast-arriving streams. In a supervised learning setting, instances in these streams are labeled as belonging to a particular class. When the number of classes in the data stream is more than two, such a data stream is referred to as a multi-class data stream. Multi-class imbalanced data stream describes the situation where the instance distribution of the classes is skewed, such that instances of some classes occur more frequently than others. Classes with the frequently occurring i
... Continue Reading
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