Using genetic programming for the induction of oblique decision trees
In this paper, we present a genetically induced oblique decision tree algorithm. In traditional decision tree, each internal node has a testing criterion involving a single attribute. Oblique decision tree allows testing criterion to consist of more than one attribute. Here we use genetic programmin...
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| creator | Shali, A. Kangavari, M.R. Bina, B. |
| description | In this paper, we present a genetically induced oblique decision tree algorithm. In traditional decision tree, each internal node has a testing criterion involving a single attribute. Oblique decision tree allows testing criterion to consist of more than one attribute. Here we use genetic programming to evolve and find an optimal testing criterion in each internal node for the set of samples at that node. This testing criterion is the characteristic function of a relation over existing attributes. We present the algorithm for construction of the oblique decision tree. We also compare the results of our proposed oblique decision tree with the one of C4.5 algorithm. |
| doi_str_mv | 10.1109/ICMLA.2007.66 |
| format | Conference Proceeding |
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We also compare the results of our proposed oblique decision tree with the one of C4.5 algorithm.</description><subject>Application software</subject><subject>Arithmetic</subject><subject>Decision trees</subject><subject>Genetic algorithms</subject><subject>Genetic engineering</subject><subject>Genetic programming</subject><subject>Machine learning</subject><subject>Machine learning algorithms</subject><subject>Partitioning algorithms</subject><subject>Testing</subject><isbn>9780769530697</isbn><isbn>0769530699</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2007</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotjD1PwzAURS0hJFDJyMTiP5Dw_G2PVVSgUioWOlep_RKMmqTY6cC_JxXcM1zp6OoS8sigYgzc87beNeuKA5hK6xtSOGPBaKcEaGfuSJHzFyyRijED92Szz3HsaY8jztHTc5r61A7D1XVTovMn0jiGi5_jNNKpo9PxFL8vSAP6mK9uToj5gdx27Slj8d8rsn_ZfNRvZfP-uq3XTRkZqLn0PCjfeSe590EogdZC13LJwCAGdIYztJJrsaA55yCs8oYdbViGrA1iRZ7-fiMiHs4pDm36OUipDAclfgGT70kP</recordid><startdate>200712</startdate><enddate>200712</enddate><creator>Shali, A.</creator><creator>Kangavari, M.R.</creator><creator>Bina, B.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200712</creationdate><title>Using genetic programming for the induction of oblique decision trees</title><author>Shali, A. ; Kangavari, M.R. ; Bina, B.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i105t-c2d5cfc942ccd353e880fa24107eede9721e8426363662220385c71b8d8801ad3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2007</creationdate><topic>Application software</topic><topic>Arithmetic</topic><topic>Decision trees</topic><topic>Genetic algorithms</topic><topic>Genetic engineering</topic><topic>Genetic programming</topic><topic>Machine learning</topic><topic>Machine learning algorithms</topic><topic>Partitioning algorithms</topic><topic>Testing</topic><toplevel>online_resources</toplevel><creatorcontrib>Shali, A.</creatorcontrib><creatorcontrib>Kangavari, M.R.</creatorcontrib><creatorcontrib>Bina, B.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Shali, A.</au><au>Kangavari, M.R.</au><au>Bina, B.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Using genetic programming for the induction of oblique decision trees</atitle><btitle>Sixth International Conference on Machine Learning and Applications (ICMLA 2007)</btitle><stitle>ICMLA</stitle><date>2007-12</date><risdate>2007</risdate><spage>38</spage><epage>43</epage><pages>38-43</pages><isbn>9780769530697</isbn><isbn>0769530699</isbn><abstract>In this paper, we present a genetically induced oblique decision tree algorithm. In traditional decision tree, each internal node has a testing criterion involving a single attribute. Oblique decision tree allows testing criterion to consist of more than one attribute. Here we use genetic programming to evolve and find an optimal testing criterion in each internal node for the set of samples at that node. This testing criterion is the characteristic function of a relation over existing attributes. We present the algorithm for construction of the oblique decision tree. We also compare the results of our proposed oblique decision tree with the one of C4.5 algorithm.</abstract><pub>IEEE</pub><doi>10.1109/ICMLA.2007.66</doi><tpages>6</tpages></addata></record> |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Application software Arithmetic Decision trees Genetic algorithms Genetic engineering Genetic programming Machine learning Machine learning algorithms Partitioning algorithms Testing |
| title | Using genetic programming for the induction of oblique decision trees |
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