Computing and Information Systems - Research Publications
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ItemTraining Robust Models with Random Projection(IEEE, 2016)Regularization plays an important role in machine learning systems. We propose a novel methodology for model regularization using random projection. We demonstrate the technique on neural networks, since such models usually comprise a very large number of parameters, calling for strong regularizers. It has been shown recently that neural networks are sensitive to two kinds of samples: (i) adversarial samples, which are generated by imperceptible perturbations of previously correctly-classified samples - yet the network will misclassify them; and (ii) fooling samples, which are completely unrecognizable, yet the network will classify them with extremely high confidence. In this paper, we show how robust neural networks can be trained using random projection. We show that while random projection acts as a strong regularizer, boosting model accuracy similar to other regularizers, such as weight decay and dropout, it is far more robust to adversarial noise and fooling samples. We further show that random projection also helps to improve the robustness of traditional classifiers, such as Random Forrest and Gradient Boosting Machines.
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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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ItemCombining real and virtual graphs to enhance data clustering(IEEE, 2010-11-18)
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ItemScoreFinder: A Method for Collaborative Quality Inference on User-Generated Content(IEEE COMPUTER SOC, 2010)
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ItemEP-based robust weighting scheme for fuzzy SVMS(Australian Computer Society, 2010-12-01)
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ItemMining Distribution Change in Stock Order Streams(IEEE COMPUTER SOC, 2010)
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ItemiVAT and aVAT: Enhanced Visual Analysis for Cluster Tendency Assessment(SPRINGER-VERLAG BERLIN, 2010)
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ItemA Novel Scalable Multi-class ROC for Effective Visualization and Computation(SPRINGER-VERLAG BERLIN, 2010)