By Bi-Ru Dai, Shu-Ming Hsu (auth.), Joshua Zhexue Huang, Longbing Cao, Jaideep Srivastava (eds.)
The two-volume set LNAI 6634 and 6635 constitutes the refereed complaints of the fifteenth Pacific-Asia convention on wisdom Discovery and knowledge Mining, PAKDD 2011, held in Shenzhen, China in could 2011.
The overall of 32 revised complete papers and fifty eight revised brief papers have been rigorously reviewed and chosen from 331 submissions. The papers current new rules, unique learn effects, and useful improvement studies from all KDD-related parts together with facts mining, computer studying, synthetic intelligence and trend reputation, information warehousing and databases, data, knoweldge engineering, habit sciences, visualization, and rising parts akin to social community analysis.
Read Online or Download Advances in Knowledge Discovery and Data Mining: 15th Pacific-Asia Conference, PAKDD 2011, Shenzhen, China, May 24-27, 2011, Proceedings, Part I PDF
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Additional info for Advances in Knowledge Discovery and Data Mining: 15th Pacific-Asia Conference, PAKDD 2011, Shenzhen, China, May 24-27, 2011, Proceedings, Part I
Hart, S. ) Handbook of Game Theory, vol. 2. V, Amsterdam (1994) 9. : An introduction to variable and feature selection. Journal of Machine Learning Research 3, 1157–1182 (2003) 10. : An O(n2 ) algorithm for minimum cuts. In: STOC (1993) A Game Theoretic Approach for Feature Clustering and Its Application 25 11. : Axiomatic scalable neurocontroller analysis via shapley value. Artiﬁcial Life 12 (2006) 12. : Wrappers for feature subset selection. Artiﬁcial Intelligence 77, 273–324 (1997) 13. : A tutorial on spectral clustering.
We can neither find any explanation why these lead to the best number nor do we have any formal feature selection model to obtain this number. In this paper, we conduct an in-depth empirical analysis and argue that simply selecting the features with the highest scores may not be the best strategy. A highest scores approach will turn many documents into zero length, so that they cannot contribute to the training process. Accordingly, we formulate the feature selection process as a dual objective optimization problem, and identify the best number of features for each document automatically.
Yet, how inferior is it? By fine tuning the text preprocessing, we will see that their differences are lower than expected. The rest of this paper is organized as follows. Section 2 presents the issues related to feature selection; Section 4 discusses existing works; Section 3 reports the experimental results; Section 5 concludes this paper. g. ) is. In this paper, we move the research in this line a step forward by not only relating the classifier performances on the number of features that have to be selected, but also attempt to answer the question of why such a number would be optimal.