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Handling both continuous and discrete attributes - In order to handle continuous attributes, C4.5 creates a threshold and then splits the list into those whose attribute value is above the threshold and those that are less than or equal to
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At each node of the tree, C4.5 chooses the attribute of the data that most effectively splits its set of samples into subsets enriched in one class or the other. The splitting criterion is the normalized
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Handling training data with missing attribute values - C4.5 allows attribute values to be marked as ? for missing. Missing attribute values are simply not used in gain and entropy calculations.
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machine learning software described the C4.5 algorithm as "a landmark decision tree program that is probably the machine learning workhorse most widely used in practice to date".
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Quinlan went on to create C5.0 and See5 (C5.0 for Unix/Linux, See5 for
Windows) which he markets commercially. C5.0 offers a number of improvements on C4.5. Some of these are:
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Pruning trees after creation - C4.5 goes back through the tree once it's been created and attempts to remove branches that do not help by replacing them with leaf nodes.
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All the samples in the list belong to the same class. When this happens, it simply creates a leaf node for the decision tree saying to choose that class.
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None of the features provide any information gain. In this case, C4.5 creates a decision node higher up the tree using the expected value of the class.
383:(difference in entropy). The attribute with the highest normalized information gain is chosen to make the decision. The C4.5 algorithm then
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Instance of previously unseen class encountered. Again, C4.5 creates a decision node higher up the tree using the expected value.
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103:. The decision trees generated by C4.5 can be used for classification, and for this reason, C4.5 is often referred to as a
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J. R. Quinlan. Improved use of continuous attributes in c4.5. Journal of
Artificial Intelligence Research, 4:77-90, 1996.
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Smaller decision trees - C5.0 gets similar results to C4.5 with considerably smaller decision trees.
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S.B. Kotsiantis, "Supervised
Machine Learning: A Review of Classification Techniques",
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Weighting - C5.0 allows you to weight different cases and misclassification types.
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Speed - C5.0 is significantly faster than C4.5 (several orders of magnitude)
605:"Data Mining: Practical machine learning tools and techniques, 3rd Edition"
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Source for a single-threaded Linux version of C5.0 is available under the
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C4.5 builds decision trees from a set of training data in the same way as
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M. Kuhn and K. Johnson, Applied
Predictive Modeling, Springer 2013
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C4.5 made a number of improvements to ID3. Some of these are:
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be the attribute with the highest normalized information gain.
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539:- Boosting improves the trees and gives them more accuracy.
421:, the general algorithm for building decision trees is:
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Memory usage - C5.0 is more memory efficient than C4.5
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Original implementation on Ross
Quinlan's homepage:
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the attributes to remove those that may be unhelpful.
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Modifying C4.5 to generate temporal and causal rules
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457:Recurse on the sublists obtained by splitting on
603:Ian H. Witten; Eibe Frank; Mark A. Hall (2011).
114:It became quite popular after ranking #1 in the
607:. Morgan Kaufmann, San Francisco. p. 191.
348:of the sample, as well as the class in which
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484:implementation of the C4.5 algorithm in the
201:of already classified samples. Each sample
99:. C4.5 is an extension of Quinlan's earlier
618:Umd.edu - Top 10 Algorithms in Data Mining
510:Handling attributes with differing costs.
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69:Learn how and when to remove this message
545:Winnowing - a C5.0 option automatically
32:This article includes a list of general
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461:, and add those nodes as children of
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593:. Morgan Kaufmann Publishers, 1993.
518:Improvements in C5.0/See5 algorithm
228:consists of a p-dimensional vector
194:{\displaystyle S={s_{1},s_{2},...}}
91:is an algorithm used to generate a
16:Algorithm for making decision trees
675:http://www.rulequest.com/Personal/
38:it lacks sufficient corresponding
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116:Top 10 Algorithms in Data Mining
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495:Improvements from ID3 algorithm
425:Check for the above base cases.
118:pre-eminent paper published by
651:Is See5/C5.0 Better Than C4.5?
344:represent attribute values or
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141:. The training data is a set
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591:Programs for Machine Learning
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555:GNU General Public License
107:. In 2011, authors of the
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394:This algorithm has a few
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630:Informatica
489:data mining
479:open source
389:partitioned
51:introducing
689:Categories
578:References
419:pseudocode
413:Pseudocode
396:base cases
391:sublists.
34:references
129:Algorithm
125:in 2008.
59:July 2008
561:See also
537:boosting
385:recurses
346:features
120:Springer
557:(GPL).
547:winnows
387:on the
375:falls.
47:improve
491:tool.
477:is an
459:a_best
452:a_best
441:a_best
36:, but
486:Weka
482:Java
463:node
448:node
439:Let
123:LNCS
109:Weka
89:C4.5
504:it.
475:J48
417:In
135:ID3
691::
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Text is available under the Creative Commons Attribution-ShareAlike License. Additional terms may apply.
