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The genome of the mesopolyploid crop species Brassica rapa
We report the annotation and analysis of the draft genome sequence of Brassica rapa accession Chiifu-401-42, a Chinese cabbage. We modeled 41,174 protein coding genes in the B. rapa genome, which hasExpand
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Mining concept-drifting data streams using ensemble classifiers
We train an ensemble of classification models, such as C4.5, RIPPER, naive Beyesian, etc., from sequential chunks of the data stream using weighted ensemble classifiers. Expand
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AdaCost: Misclassification Cost-Sensitive Boosting
AdaCost, a variant of AdaBoost, is a misclassification cost-sensitive boosting method. Expand
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Resolving conflicts in heterogeneous data by truth discovery and source reliability estimation
In many applications, one can obtain descriptions about the same objects or events from a variety of sources. Expand
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ViST: a dynamic index method for querying XML data by tree structures
We propose ViST, a novel index structure for searching XML documents that uses tree structures as the basic unit of query to avoid expensive join operations. Expand
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Mining big data: current status, and forecast to the future
Big Data is a new term used to identify datasets that we can not manage with current methodologies or data mining software tools due to their large size and complexity. Expand
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Cost-based modeling for fraud and intrusion detection: results from the JAM project
We describe the results achieved using the JAM distributed data mining system for the real world problem of fraud detection in financial information systems. Expand
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Toward Cost-Sensitive Modeling for Intrusion Detection and Response
We define cost models to formulate the total expected cost of an IDS, and present cost-sensitive machine learning techniques that can produce detection models that are optimized for user-defined cost metrics. Expand
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A Confidence-Aware Approach for Truth Discovery on Long-Tail Data
We propose a confidence-aware truth discovery (CATD) method to automatically detect truths from conflicting data with long-tail phenomenon. Expand
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Knowledge transfer via multiple model local structure mapping
We propose a locally weighted ensemble framework to combine multiple models for transfer learning, where the weights are dynamically assigned according to a model's predictive power on each test example. Expand
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