Advances in Logic Based Intelligent Systems: Selected Papers by K. Nakamatsu, M. Abe (Editors)

By K. Nakamatsu, M. Abe (Editors)

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1. Data Preprocessing Formally, data preprocessing is a procedure to clean and transform the data before it is passed to other modelling procedure. , reducing the dimensionality of the input space. As data cleaning is very straightforward of applying standard process of “zero mean and unit variance”, the concentration is put on data transformation. The following subsections introduce the common data transformation methods [5, 8]. 1 Principal Component Analysis A well-known and frequently used technique for dimensionality reduction for input space is linear Principal Component Analysis (PCA).

Here seven membership functions of triangular type are uniformly distributed in an interval [xmax , xmin ] of input domain. The values of the membership functions are based in the experiments of Shaw et al. [5], which show that a change from five to seven triangular sets increases the precision in about 15%. There are no significative improvements for greater number of sets. We note that equidistant membership functions may not be convenient in situations where there are concentration of patterns in some regions and dispersion in others [5].

Burges, H. Drucker, S. Golowich, L. Van Hemmen, K. Muller, B. Scholkopf, V. Vapnik. Regression Estimation with Support Vector Learning Machines, 1996. de/~smola [2] C. Bishop. Neural Networks for Pattern Recognition. Oxford University Press. , 1995. [3] D. Pyle. Data Preparation for Data Mining. Morgan Kaufmann, 1999. [4] D. Borowiak. Model Discrimination for Nonlinear Regression Models. Marcel Dekker, 1989. [5] J. Suykens, T. Gestel, J. De Brabanter, B. De Moor, and J. Vandewalle. Least Squares Support Vector Machines.

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