Download Data Mining with Decision Trees: Theory and Applications by Lior Rokach PDF

By Lior Rokach

Choice timber became some of the most robust and renowned techniques in wisdom discovery and knowledge mining; it's the technological know-how of exploring huge and complicated our bodies of knowledge with the intention to become aware of important styles. choice tree studying maintains to adapt through the years. present equipment are continually being greater and new tools introduced.

This 2d version is devoted fullyyt to the sphere of choice timber in information mining; to hide all facets of this significant process, in addition to enhanced or new tools and methods constructed after the book of our first version. during this new version, all chapters were revised and new themes introduced in. New issues contain Cost-Sensitive energetic studying, studying with doubtful and Imbalanced information, utilizing choice bushes past type initiatives, privateness protecting determination Tree studying, classes realized from Comparative reports, and studying determination timber for giant facts. A walk-through consultant to latest open-source facts mining software program is additionally integrated during this version.

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Extra info for Data Mining with Decision Trees: Theory and Applications (2nd Edition)

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If a line passes through a point on the convex hull, then there is no other line with the same slope passing through another point with a larger TP intercept. Thus, the classifier at that point is optimal under any distribution assumptions in tandem with that slope. 4 1 Fig. 4 A typical ROC curve. 2 19:12 Data Mining with Decision Trees (2nd Edition) - 9in x 6in b1856-ch04 Data Mining with Decision Trees Hit-Rate Curve The hit-rate curve presents the hit ratio as a function of the quota size. Hitrate is calculated by counting the actual positive labeled instances inside a determined quota [An and Wang (2001)].

Induction of an optimal decision tree from a given data is considered to be a difficult task. Hancock et al. (1996) have shown that finding a minimal decision tree consistent with the training set is NP-hard while Hyafil and Rivest (1976) have demonstrated that constructing a minimal binary tree with respect to the expected number of tests required for classifying an unseen instance is NP-complete. Even finding the minimal equivalent decision tree for a given decision tree [Zantema and Bodlaender (2000)] or building the optimal decision tree from decision tables is known to be NP-hard [Naumov (1991)].

5 and CART). Other inducers perform only the growing phase. 1 presents a typical pseudo code for a top-down inducing algorithm of a decision tree using growing and pruning. Note that these page 28 August 18, 2014 19:12 Data Mining with Decision Trees (2nd Edition) - 9in x 6in b1856-ch03 A Generic Algorithm for Top-Down Induction of Decision Trees page 29 29 TreeGrowing (S,A,y,SplitCriterion,StoppingCriterion) Where: S - Training Set A - Input Feature Set y - Target Feature SplitCriterion --- the method for evaluating a certain split StoppingCriterion --- the criteria to stop the growing process Create a new tree T with a single root node.

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