Advances in Data Mining. Applications and Theoretical by Claus Weihs, Gero Szepannek (auth.), Petra Perner (eds.)

By Claus Weihs, Gero Szepannek (auth.), Petra Perner (eds.)

This publication constitutes the refereed court cases of the ninth commercial convention on info Mining, ICDM 2009, held in Leipzig, Germany in July 2009.

The 32 revised complete papers offered have been conscientiously reviewed and chosen from one hundred thirty submissions. The papers are equipped in topical sections on facts mining in drugs and agriculture, info mining in advertising, finance and telecommunication, info mining in method keep watch over, and society, facts mining on multimedia information and theoretical features of information mining.

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3, there is an equally big jump in accuracy for both training and testing data when moving to the simple rule above, which has a size of about 34 bits according to ADATE’s built in syntactic complexity measure. 4% on the test data and a 95% confidence interval between 77% and 94%. Note that WEKA was run using ten-fold cross validation, which means that 90% of the data were used for training instead of only 50% as in the ADATE experiments. But even if ADATE was given much less training data, it still created results comparable with those of WEKA given in Table 2 and additionally a very simple model that is easy to understand and use for optimization of the Enose.

This is certainly a perfect tree for the training data but is likely to be too specific – the problem of overlearning occurs. For new, unseen data, such a specific tree will probably have a high prediction error. Therefore, regression trees are usually pruned to a specific depth which is a trade-off between high accuracy and high generality. This can easily be achieved by setting a lower bound for the number of instances covered by a single node below which no split should occur. For this work the standard matlab implementation of classregtree was used.

A brief summary of the available data attributes for both data sets is given in Tables 1(a) to 1(c). On each field, different fertilization strategies have been used. One of those strategies is based on a technique that uses a multi-layer perceptron (MLP) for prediction and optimization. , [25,26] or [32]. For each field, one data set will contain all records, thus containing all the different fertilization strategies. In addition, a subset of F131 has been chosen to serve as a fourth data set to be evaluated.

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