- Computing and Information Systems - Research Publications
Computing and Information Systems - Research Publications
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ItemImproving the quality of explanations with local embedding perturbationsJia, Y ; Bailey, J ; Ramamohanarao, K ; Leckie, C ; Houle, ME (ACM, 2019-07-25)Classifier explanations have been identified as a crucial component of knowledge discovery. Local explanations evaluate the behavior of a classifier in the vicinity of a given instance. A key step in this approach is to generate synthetic neighbors of the given instance. This neighbor generation process is challenging and it has considerable impact on the quality of explanations. To assess quality of generated neighborhoods, we propose a local intrinsic dimensionality (LID) based locality constraint. Based on this, we then propose a new neighborhood generation method. Our method first fits a local embedding/subspace around a given instance using the LID of the test instance as the target dimensionality, then generates neighbors in the local embedding and projects them back to the original space. Experimental results show that our method generates more realistic neighborhoods and consequently better explanations. It can be used in combination with existing local explanation algorithms.
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ItemTraining Robust Models with Random ProjectionNguyen, XV ; Monazam Erfani, S ; Paisitkriangkrai, S ; Bailey, J ; Leckie, C ; Ramamohanarao, K (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 dataKOTAGIRI, R ; Hassan, MR ; Jin, V (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 Weighting Scheme Based on Emerging Patterns for Weighted Support Vector MachinesFAN, HONGJIAN ; Kotagiri, Ramamohanarao ( 2005)Support Vector Machines (SVMs) are powerful tools for solving classification problems and have been applied to many application fields, such as pattern recognition and data mining, in the past decade. Weighted Support Vector Machines (weighted SVMs) extend SVMs by considering that different input vectors make different contributions to the learning of decision surface. An important issue in training weighted SVMs is how to develop a reliable weighting model to reflect the true noise distribution in the training data, i.e., noise and outliers should have low weights. In this paper we propose to use Emerging Patterns (EPs) to construct such a model. EPs are those itemsets whose supports in one class are significantly higher than their supports in the other class. Since EPs of a given class represent the discriminating knowledge unique to their home class, noise and outliers should contain no EPs or EPs of the both contradicting classes, while a representative instance of the class should contain strong EPs of the same class. We calculate numeric scores for each instance based on EPs, and then assign weights to the training data using those scores. An extensive experiments carried out on a large number of benchmark datasets show that our weighting scheme often improves the performance of weighted SVMs over SVMs. We argue that the improvement is due to the ability of our model to approximate the true distribution of data points.
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ItemAnalysis and Enhancement of On-demand Routing in Wireless Sensor NetworksDallas, DP ; Leckie, CA ; Ramamohanarao, K (ASSOC COMPUTING MACHINERY, 2008)
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ItemARTS: Agent-oriented robust transactional systemWang, M ; Unruh, A ; Ramamohanarao, K (ACM, 2007-12-01)
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ItemExpanding the Training Data Space Using Emerging Patterns and Genetic MethodsKOTAGIRI, R. ; AL HAMMADY, H. (Society for Industrial and Applied Mathematics, 2005)
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ItemProactive Traffic Merging Strategies for Sensor-Enabled CarsWang, Z ; Kulik, L ; Ramamohanarao, K ; Holfelder, W ; Santi, P ; Hu, Y-C ; Hubaux, J-P (ASSOC COMPUTING MACHINERY, 2007)
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ItemSemantic-compensation-based recovery in multi-agent systemsUnruh, A ; Harjadi, H ; Bailey, J ; Ramamohanarai, K (IEEE, 2005)
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ItemRoleVAT: Visual Assessment of Practical Need for Role Based Access ControlZhang, D ; Ramamohanarao, K ; Versteeg, S ; Zhang, R (IEEE COMPUTER SOC, 2009)