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Search Results: 1 - 6 of 6 matches for " Zhana Kuncheva "
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Community detection in multiplex networks using locally adaptive random walks
Zhana Kuncheva,Giovanni Montana
Computer Science , 2015,
Abstract: Multiplex networks, a special type of multilayer networks, are increasingly applied in many domains ranging from social media analytics to biology. A common task in these applications concerns the detection of community structures. Many existing algorithms for community detection in multiplexes attempt to detect communities which are shared by all layers. In this article we propose a community detection algorithm, LART (Locally Adaptive Random Transitions), for the detection of communities that are shared by either some or all the layers in the multiplex. The algorithm is based on a random walk on the multiplex, and the transition probabilities defining the random walk are allowed to depend on the local topological similarity between layers at any given node so as to facilitate the exploration of communities across layers. Based on this random walk, a node dissimilarity measure is derived and nodes are clustered based on this distance in a hierarchical fashion. We present experimental results using networks simulated under various scenarios to showcase the performance of LART in comparison to related community detection algorithms.
K-NS: Section-Based Outlier Detection in High Dimensional Space
Zhana Bao
Computer Science , 2014,
Abstract: Finding rare information hidden in a huge amount of data from the Internet is a necessary but complex issue. Many researchers have studied this issue and have found effective methods to detect anomaly data in low dimensional space. However, as the dimension increases, most of these existing methods perform poorly in detecting outliers because of "high dimensional curse". Even though some approaches aim to solve this problem in high dimensional space, they can only detect some anomaly data appearing in low dimensional space and cannot detect all of anomaly data which appear differently in high dimensional space. To cope with this problem, we propose a new k-nearest section-based method (k-NS) in a section-based space. Our proposed approach not only detects outliers in low dimensional space with section-density ratio but also detects outliers in high dimensional space with the ratio of k-nearest section against average value. After taking a series of experiments with the dimension from 10 to 10000, the experiment results show that our proposed method achieves 100% precision and 100% recall result in the case of extremely high dimensional space, and better improvement in low dimensional space compared to our previously proposed method.
Finding Inner Outliers in High Dimensional Space
Zhana Bao
Computer Science , 2014,
Abstract: Outlier detection in a large-scale database is a significant and complex issue in knowledge discovering field. As the data distributions are obscure and uncertain in high dimensional space, most existing solutions try to solve the issue taking into account the two intuitive points: first, outliers are extremely far away from other points in high dimensional space; second, outliers are detected obviously different in projected-dimensional subspaces. However, for a complicated case that outliers are hidden inside the normal points in all dimensions, existing detection methods fail to find such inner outliers. In this paper, we propose a method with twice dimension-projections, which integrates primary subspace outlier detection and secondary point-projection between subspaces, and sums up the multiple weight values for each point. The points are computed with local density ratio separately in twice-projected dimensions. After the process, outliers are those points scoring the largest values of weight. The proposed method succeeds to find all inner outliers on the synthetic test datasets with the dimension varying from 100 to 10000. The experimental results also show that the proposed algorithm can work in low dimensional space and can achieve perfect performance in high dimensional space. As for this reason, our proposed approach has considerable potential to apply it in multimedia applications helping to process images or video with large-scale attributes.
Robust Subspace Outlier Detection in High Dimensional Space
Zhana Bao
Computer Science , 2014,
Abstract: Rare data in a large-scale database are called outliers that reveal significant information in the real world. The subspace-based outlier detection is regarded as a feasible approach in very high dimensional space. However, the outliers found in subspaces are only part of the true outliers in high dimensional space, indeed. The outliers hidden in normal-clustered points are sometimes neglected in the projected dimensional subspace. In this paper, we propose a robust subspace method for detecting such inner outliers in a given dataset, which uses two dimensional-projections: detecting outliers in subspaces with local density ratio in the first projected dimensions; finding outliers by comparing neighbor's positions in the second projected dimensions. Each point's weight is calculated by summing up all related values got in the two steps projected dimensions, and then the points scoring the largest weight values are taken as outliers. By taking a series of experiments with the number of dimensions from 10 to 10000, the results show that our proposed method achieves high precision in the case of extremely high dimensional space, and works well in low dimensional space.
Pre-Selection of Independent Binary Features: An Application to Diagnosing Scrapie in Sheep
Ludmila Kuncheva,C. Whitaker,P. Cockcroft,Z. S. Hoare
Computer Science , 2012,
Abstract: Suppose that the only available information in a multi-class problem are expert estimates of the conditional probabilities of occurrence for a set of binary features. The aim is to select a subset of features to be measured in subsequent data collection experiments. In the lack of any information about the dependencies between the features, we assume that all features are conditionally independent and hence choose the Naive Bayes classifier as the optimal classifier for the problem. Even in this (seemingly trivial) case of complete knowledge of the distributions, choosing an optimal feature subset is not straightforward. We discuss the properties and implementation details of Sequential Forward Selection (SFS) as a feature selection procedure for the current problem. A sensitivity analysis was carried out to investigate whether the same features are selected when the probabilities vary around the estimated values. The procedure is illustrated with a set of probability estimates for Scrapie in sheep.
Journal of Central European Agriculture , 2012, DOI: 10.5513/jcea01/13.2.1059
Abstract: The aim of this study was to characterize drought tolerance of 20 common bean genotypes using some biochemical markers for oxidative stress. 10 common bean cultivars (9 Bulgarian and a Mexican - BAT 477) and 10 mutant lines M(19–20), previously obtained by us after the treatment of seeds from Dobroudjanski 2 and Dobroudjanski 7 cultivars with ethyl methan sulphonate (EMS) and N-nithroso-N-ethyl urea (NEU) were used in this investigation. BAT 477 was chosen as a control and it was presented in unique cluster group. Three biochemical markers – malondialdehyde (MDA), hydrogen peroxide (H2O2) and proline were analyzed. The results were statistically elaborated by mono-, bifactorial ANOVA and cluster analyses. Our preliminary results demonstrated that to obtain more valuable information, concerning drought tolerance of both common bean cultivars and mutant lines, MDA, H2O2 and proline should be used as early warning markers. Genotypes studied could be of interest in future investigations being a geneplasme source of common bean drought tolerance.
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