Computing and Information Systems - Research Publications
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ItemA new approach to enhance the performance of decision tree for classifying gene expression data(BMC Proceedings, 2013)BACKGROUND: Gene expression data classification is a challenging task due to the large dimensionality and very small number of samples. Decision tree is one of the popular machine learning approaches to address such classification problems. However, the existing decision tree algorithms use a single gene feature at each node to split the data into its child nodes and hence might suffer from poor performance specially when classifying gene expression dataset. RESULTS: By using a new decision tree algorithm where, each node of the tree consists of more than one gene, we enhance the classification performance of traditional decision tree classifiers. Our method selects suitable genes that are combined using a linear function to form a derived composite feature. To determine the structure of the tree we use the area under the Receiver Operating Characteristics curve (AUC). Experimental analysis demonstrates higher classification accuracy using the new decision tree compared to the other existing decision trees in literature. CONCLUSION: We experimentally compare the effect of our scheme against other well known decision tree techniques. Experiments show that our algorithm can substantially boost the classification performance of the decision tree.
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ItemA voting approach to identify a small number of highly predictive genes using multiple classifiers(BMC, 2009-01-30)BACKGROUND: Microarray gene expression profiling has provided extensive datasets that can describe characteristics of cancer patients. An important challenge for this type of data is the discovery of gene sets which can be used as the basis of developing a clinical predictor for cancer. It is desirable that such gene sets be compact, give accurate predictions across many classifiers, be biologically relevant and have good biological process coverage. RESULTS: By using a new type of multiple classifier voting approach, we have identified gene sets that can predict breast cancer prognosis accurately, for a range of classification algorithms. Unlike a wrapper approach, our method is not specialised towards a single classification technique. Experimental analysis demonstrates higher prediction accuracies for our sets of genes compared to previous work in the area. Moreover, our sets of genes are generally more compact than those previously proposed. Taking a biological viewpoint, from the literature, most of the genes in our sets are known to be strongly related to cancer. CONCLUSION: We show that it is possible to obtain superior classification accuracy with our approach and obtain a compact gene set that is also biologically relevant and has good coverage of different biological processes.
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ItemInformation security culture as an enabler: addressing the gap between organisational knowledge sharing and information security( 2014-08-01)Knowledge sharing is a vital business strategy that creates value for an organisation. It also leads to accidental or deliberate loss of information and knowledge. With an ideal culture, the knowledge sharing barrier can be broken without leaking information. We gathered data from the literature on the benefits of knowledge sharing in organisations and the related risks, addressing the role of a positive organisational culture. We interviewed information security specialists in small and large organisations in Melbourne and overseas. The study confirms the findings from literature that organisations value knowledge sharing to gain a competitive advantage. They also revealed that the preventive measures of knowledge leakage usually involved fostering a sharing culture with strategy, policies and controls in place with regular training and awareness. Based on these observations, we propose the need for future research on organisations that have fostered a culture of sharing knowledge without compromising its security.
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ItemPhylogeny Inference Using a Multi-objective Evolutionary Algorithm with Indirect Representation(SPRINGER-VERLAG BERLIN, 2008)
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ItemHMM based fuzzy model for time series prediction(IEEE, 2006)
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ItemROC-tree: A novel decision tree induction algorithm based on receiver operating characteristics to classify gene expression data(Society for Industrial and Applied Mathematics, 2008-01-01)
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ItemImproving k-Nearest Neighbour Classification with Distance Functions Based on Receiver Operating Characteristics(SPRINGER-VERLAG BERLIN, 2008)
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ItemA Novel Scalable Multi-class ROC for Effective Visualization and Computation(SPRINGER-VERLAG BERLIN, 2010)