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Comparison of feature selection techniques in real and synthetic data

Heikamp, F. (2015) Comparison of feature selection techniques in real and synthetic data. Bachelor's Thesis, Computing Science.

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Feature selection is a process used for selecting a subset of features from the feature space of a dataset, according to some criteria. The main goals of feature selection are creating simpler and/or better models and getting insights about the data. The problem is that the accuracy of different feature selection techniques might be dependent on the classifier or the dataset used. In this thesis different feature selection techniques are compared with each other, each feature selection technique is tested with a number of different classifiers and datasets, which either may be real or synthetic data. By providing these insights, we support practitioners in machine learning and classification with a better understanding of the relative advantages and challenges of several feature selection methods, and thereby arguably help them in the process of classifier design.

Item Type: Thesis (Bachelor's Thesis)
Degree programme: Computing Science
Thesis type: Bachelor's Thesis
Language: English
Date Deposited: 15 Feb 2018 08:05
Last Modified: 15 Feb 2018 08:05

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