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Research on streaming data classification has been mostly based on the assumption that data can be fully labelled. However, this is impractical. Firstly it is impossible to make a complete labelling before all data has arrived. Secondly it is generally very expensive to obtain fully labelled data by using man power. Thirdly user interests may change with time so the labels issued earlier may be inconsistent...
This paper presents a novel approach to feature construction for structured data in order to enhance graph prediction classification performance. To this end we combine graph mining techniques with graph kernel based classifiers. The main idea is to employ efficient mining techniques to extract a set of patterns correlated with the target concept and use these, or a selected subset of these, to annotate...
This paper addresses the identification problem of causal variables for the system anomaly. In real-world complicated systems, even experts often fail to specify causal factors, thus they attempt to detect the anomaly with exploratory heuristics. Our goal is to offer further information that supports anomaly cause analysis using the incomplete empirical knowledge. Proposed technique discovers responsible...
This paper presents a method to discover the discriminative patterns or features in hyperspectral data for classification. The proposed method searches the data space along both spectral and spatial frequency axis and combines the adjacent spectral and spatial frequency bands so that a simpler but more effective feature set is achieved. The algorithm is tested on hyperspectral images of hazelnut kernels...
Detection of anomalies in multivariate time series is an important data mining task with potential applications in medical diagnosis, ecosystem modeling, and network traffic monitoring. In this paper, we present a robust graph-based algorithm for detecting anomalies in noisy multivariate time series data. A key feature of the algorithm is the alignment of kernel matrices constructed from the time...
We present and discuss several spatiotemporal kernels designed to mine real-life and simulated data in support of drought prediction. We implement and empirically validate these kernels for support vector machines. Issues related to the nature of geographic data such as autocorrelation and directionality are investigated.
This paper focuses on developing classification algorithms for problems in which there is a need to predict the class based on multiple observations (examples) of the same phenomenon (class). These problems give rise to a new classification problem, referred to as set classification, that requires the prediction of a set of instances given the prior knowledge that all the instances of the set belong...
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