Abstract
The accuracy of a classifier relies heavily on the encoding and representation of input data. Many machine learning algorithms require that the input vectors be composed of numeric values on which arithmetic and comparison operators be applied. However, many real life applications involve the collection of data, which is symbolic or ‘nominal type’ data, on which these operators are not available. This paper presents a framework called logical expression feature transformation (LEFT), which can be used for mapping symbolic attributes to a continuous domain, for further processing by a learning machine. It is a generic method that can be used with any suitable clustering method and any appropriate distance metric. The proposed method was tested on synthetic and real life datasets. The results show that this framework not only achieves dimensionality reduction but also improves the accuracy of a classifier.
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Saeed, M. (2012). LEFT–Logical Expressions Feature Transformation: A Framework for Transformation of Symbolic Features. In: Wang, J., Yen, G.G., Polycarpou, M.M. (eds) Advances in Neural Networks – ISNN 2012. ISNN 2012. Lecture Notes in Computer Science, vol 7368. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31362-2_17
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DOI: https://doi.org/10.1007/978-3-642-31362-2_17
Publisher Name: Springer, Berlin, Heidelberg
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