Decision tree based classification algorithms like C4.5 and Explore build a single tree from a data set. The two main purposes of building a decision tree are to extract various patterns/logic-rules existing in a dataset, and to predict the class attribute value of an unlabeled record. Sometimes a set of decision trees, rather than just a single tree, is also generated from a dataset. A set of multiple trees, when used wisely, typically have better prediction accuracy on unlabeled records. Existing multiple tree techniques are catered for high dimensional data sets and therefore unable to build many trees from low dimensional data sets. In this paper we present a novel technique called Sys-For that can build many trees even from a low dimensional data set. Another strength of the technique is that instead of building multiple trees using any attribute (good or bad) it uses only those attributes that have high classification capabilities. We also present two novel voting techniques in order to predict the class value of an unlabeled record through the collective use of multiple trees. Experimental results demonstrate that SysFor is suitable for multiple pattern extraction and knowledge discovery from both low dimensional and high dimensional data sets by building a number of good quality decision trees.Moreover, it also has prediction accuracy higher than the accuracy of several existing techniques that have previously been shown as having high performance.
|Title of host publication||9th Australasian Data Mining Conference|
|Subtitle of host publication||AusDM 2011|
|Editors||V Estivill-Castro, S Simoff|
|Place of Publication||Sydney, Australia|
|Publisher||Australian Computer Society Inc|
|Number of pages||6|
|Publication status||Published - 2011|
|Event||The 9th Australasian Data Mining Conference: AusDM 2011 - University of Ballarat, Ballarat, Australia|
Duration: 01 Dec 2011 → 02 Dec 2011
|Name||Conferences in Research and Practice in Information Technology Series|
|Publisher||Australian Computer Society|
|Conference||The 9th Australasian Data Mining Conference|
|Period||01/12/11 → 02/12/11|
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