https://nova.newcastle.edu.au/vital/access/ /manager/Index ${session.getAttribute("locale")} 5 https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:31539 SysFor 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.]]> Sat 24 Mar 2018 08:44:27 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:27303 Sat 24 Mar 2018 07:38:33 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:25962 Sat 24 Mar 2018 07:37:02 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:27937 Sat 24 Mar 2018 07:36:09 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:27411 Sat 24 Mar 2018 07:35:24 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:25146 Sat 24 Mar 2018 07:17:10 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:22915 (alpha,beta)-k-Feature Set, and a ranking-based feature selection method, based on the use of CM1-scores. We employ two publicly available real-world datasets (one with training and test variants). The classification accuracies have been evaluated using a total of 49 methods from an open source data mining and machine learning package WEKA.]]> Sat 24 Mar 2018 07:16:28 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:22916 kNN graphs from DNA microarray data sets. The relationship among the genes in the kNN graph is determined by the similarity of their expression levels. The proposed method has been applied to a well known breast cancer microarray study and we highlighted the correlation of the highly ranked genes to the time to relapse of the disease. The method is readily applicable to other datasets, where the data points can be recognised in a multidimensional space. It can be applied to other networks (e.g., social networks, the Internet, etc.) with minimal modications.]]> Sat 24 Mar 2018 07:16:28 AEDT ]]>