By Ahmed Al-Ani, Amir F. Atiya (auth.), Friedhelm Schwenker, Neamat El Gayar (eds.)
This ebook constitutes the refereed court cases of the 4th IAPR TC3 Workshop, ANNPR 2010, held in Cairo, Eqypt, in April 2010. The 23 revised complete papers awarded have been rigorously reviewed and chosen from forty two submissions. the most important themes of ANNPR are supervised and unsupervised studying, function choice, development popularity in sign and picture processing, and functions in info mining or bioinformatics.
Read Online or Download Artificial Neural Networks in Pattern Recognition: 4th IAPR TC3 Workshop, ANNPR 2010, Cairo, Egypt, April 11-13, 2010. Proceedings PDF
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Additional resources for Artificial Neural Networks in Pattern Recognition: 4th IAPR TC3 Workshop, ANNPR 2010, Cairo, Egypt, April 11-13, 2010. Proceedings
The details of Correlation-Based and Causal Feature Selection Analysis 31 Table 1. Datasets Dataset Sample Features Classes Heart Disease Diabetes Hepatitis Parkinson’s Lucas Lucap 303 768 155 195 2000 2000 13 8 19 22 11 143 5 2 2 2 2 2 Missing Values Yes No Yes No No No Data type Numeric Numeric Numeric Numeric Numeric Numeric (cont. and discrete) (continuous) (cont. and discrete) (continuous) (binary) (binary) datasets are shown in Table 1. The missing data are replaced by mean and mode of that dataset.
For the thyroid data set, we measured the feature selection time conﬁning the value of C in C = [10000, 50000, 100000]. By introducing block deletion two to ﬁve times speedup was realized. Except for the blood cell and hiragana-13 data sets, KDA+BC was faster than SVM+BC. But for the hiragana-13 data set, SVM+BC was faster because only one feature was deleted. For two-class problems the KDA criterion is proved to be monotonic for the deletion of features. But for the KDA criterion for multiclass problems, it is an open problem whether the KDA criterion is monotonic.
This approach has been also analyzed in detail by  and ). In this approach the 40 A. Hefny and A. Atiya evaluation function becomes 1 − Pˆ (C(xi )|xi ), where C(xi ) is the classiﬁcation of pattern xi . The posterior probability error estimator does not need the labels (except for designing the classiﬁer) and consequently can make use of unlabeled test patterns. To further reduce variance, Hand  proposed the utlization of the marginal probability G(x) = y∈Y G(x, y), which can also be estimated using unlabeled data.