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L10basicDTalgorithm

# L10basicDTalgorithm - BASIC DECISION TREE INDUCTION...

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BASIC DECISION TREE INDUCTION ALGORITM Lecture Notes on Learning (5) cse352 Professor Anita Wasilewska Stony Brook University

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Decision Tree Algorithms Short History Late 1970s - ID3 (Interative Dichotomiser) by J. Ross Quinlan . This work expanded on earlier work on concept learning system, described by E. B. Hunt, J. Marin , and P. T. Stone . Early 1980 - C4.5 a successor of ID3 by Quinlan. C4.5 later became a benchmark to which newer supervised learning algorithms, are often compared. In 1984 , a group of statisticians ( L. Breinman, J.Friedman, R. Olshen, and C. Stone ) published the book “Classification and Regression Trees(CART) “.
Decision Tree Algorithms Short History The “Classification and Regression Trees (CART)” book described a generation of binary decision trees . ID3,C4.5 and CART were invented independently of one another yet follow a similar approach for learning decision trees from training tuples. These two cornerstone algorithms spawned a flurry of work on decision tree induction.

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Decision Tree Algorithms General Description ID3, C4.5 , and CART adopt a greedy (i.e., non- backtracking) approach. It this approach decision trees are constructed in a top-down recursive divide-and conquer manner. Most algorithms for decision tree induction also follow such a top-down approach. All of the algorithms start with a training set of tuples and their associated class labels (classification data table). The training set is recursively partitioned into smaller subsets as the tree is being built.
BASIC Decision Tree Algorithm General Description A Basic Decision Tree Algorithm presented here is as published in J.Han, M. Kamber book “Data Mining, Concepts and Techniques”, 2006 (second Edition) The algorithm may appear long, but is quite straightforward. Basic Algorithm strategy is as follows. The algorithm is called with three parameters: D, attribute_list, and Attribute_selection _ method . We refer to D as a data partition . Initially, D is the complete set of training tuples and their associated class labels (input training data).

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Basic Decision Tree Algorithms General Description The parameter attribute_list is a list of attributes describing the tuples. Attribute_seletion _method specifies a heuristic procedure for selectining the attribute that “best” descriminates the given tuples according to class. Attribute_seletion _method
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L10basicDTalgorithm - BASIC DECISION TREE INDUCTION...

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