By Matthias Studer, Gilbert Ritschard, Alexis Gabadinho, Nicolas S. Müller (auth.), Fabrice Guillet, Gilbert Ritschard, Djamel Abdelkader Zighed, Henri Briand (eds.)
During the decade, the French-speaking clinical neighborhood constructed a really robust examine task within the box of data Discovery and administration (KDM or EGC for “Extraction et Gestion des Connaissances” in French), that's fascinated with, between others, information Mining, wisdom Discovery, enterprise Intelligence, wisdom Engineering and SemanticWeb. the new and novel examine contributions amassed during this booklet are prolonged and remodeled types of a range of the simplest papers that have been initially awarded in French on the EGC 2009 convention held in Strasbourg, France on January 2009. the amount is equipped in 4 elements. half I contains 5 papers involved by way of quite a few features of supervised studying or details retrieval. half II provides 5 papers serious about unsupervised studying matters. half III comprises papers on facts streaming and on safety whereas partly IV the final 4 papers are fascinated with ontologies and semantic.
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Top-Down algorithm with pre-pruning for optimal tree search Require: T the root tree Ensure: the tree Tˆ which minimizes the proposed criterion T∗ ← T while criterion improvement do Tˆ ← T ∗ for all leaf l of the tree do T ← T∗ for all variable X of K do Search the partition rule on the leaf l according X which best improves the criterion TX ← T ∗ + PX (l) if c(TX ) < c(T ) then T ← TX end if end for if c(T ) < c(T ∗ ) then T∗ ← T end if end for end while Unfortunately, the pre-pruning algorithm creates small and under-fitted decision trees.
Coding Decision Trees. Machine Learning 11, 7–22 (1993) Classifying Very-High-Dimensional Data with Random Forests of Oblique Decision Trees Thanh-Nghi Do, Philippe Lenca, Stéphane Lallich, and Nguyen-Khang Pham Abstract. The random forests method is one of the most successful ensemble methods. However, random forests do not have high performance when dealing with very-high-dimensional data in presence of dependencies. In this case one can expect that there exist many combinations between the variables and unfortunately the usual random forests method does not effectively exploit this situation.
Indeed, the greedy approaches can deal only with low dimensional datasets due to combinatorial explosion. The OC1 approach was extended by Wu et al. (1999) by modifying the splitting criterion of the basic OC1 algorithm or by post-processing OC1 output. While these modifications outperform the basic OC1 on the correctness and the robustness to noise, the optimal hyperplanes are found with standard SVMs through the resolution of a quadratic programming. Therefore, the proposed approach has a high cost for the learning task.