Description: Decision tree learning algorithm of C4.5 algorithm is the classic, the improved ID3 algorithm, coupled with the subtree of the information, the factor attribute value can be continuous, training cases of factor property values can be of uncertain, the generated decision tree cut, reducing the size of the spanning tree.
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File list (Check if you may need any files):
Data
....\crx.data
....\crx.names
....\crx.test
....\golf.data
....\golf.names
....\hypo.data
....\hypo.names
....\hypo.test
....\labor-neg.data
....\labor-neg.names
....\labor-neg.test
....\monk1.data
....\monk1.names
....\monk1.test
....\monk2.data
....\monk2.names
....\monk2.test
....\monk3.data
....\monk3.names
....\monk3.test
....\soybean.data
....\soybean.names
....\vote.data
....\vote.names
....\vote.test
Doc
...\c4.5.1
...\c4.5rules.1
...\consult.1
...\consultr.1
...\verbose.1
...\verbrules.1
LEGAL
ReadMe
Src
...\average.c
...\besttree.c
...\build.c
...\buildex.i
...\c4.5.c
...\c4.5rules.c
...\classify.c
...\confmat.c
...\consult.c
...\consultr.c
...\contin.c
...\defns.i
...\discr.c
...\extern.i
...\genlogs.c
...\genrules.c
...\getdata.c
...\getnames.c
...\getopt.c
...\header.c
...\info.c
...\Makefile
...\makerules.c
...\Modifications
...\prune.c
...\prunerule.c
...\rules.c
...\rulex.i
...\siftrules.c
...\sort.c
...\st-thresh.c
...\stats.c
...\subset.c
...\testrules.c
...\trees.c
...\types.i
...\userint.c
...\xval-prep.c
...\xval.sh
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