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Error-reduction sampling (ERS) is a high performing (but computationally expensive) query selection strategy for active learning. Subset optimisation has been proposed to reduce computational expense by applying ERS to only a subset of examples from the pool. This paper compares techniques used to construct the subset, namely random sub-sampling and pre-filtering. We focus on pre-filtering which populates...
The reliability of an induced classifier can be affected by several factors including the data oriented factors and the algorithm oriented factors. In some cases, the reliability could also be affected by knowledge oriented factors. In this paper, we analyze three special cases to examine the reliability of the discovered knowledge. Our case study results show that (1) in the cases of mining from...
Recently, learning to rank technique has attracted much attention. However, the lack of labeled training data seriously limits its application in real-world tasks. In this paper, we propose to break this bottleneck by considering the cross-domain ldquotransfer of rank learningrdquo problem. Simultaneously, we propose a novel algorithm called TransRank, which can effectively utilize the labeled data...
Two common challenges data mining and machine learning practitioners face in many application domains are unequal classification costs and class imbalance. Most traditional data mining techniques attempt to maximize overall accuracy rather than minimize cost. When data is imbalanced, such techniques result in models that highly favor the over represented class, the class which typically carries a...
The region-classification task is to construct class regions containing the correct classes of the objects being classified with a given probability. To turn a point classifier into a region classifier, the conformal framework is used . However, applying the framework requires a non-conformity function. This function estimates the instances' non-conformity for the point classifier used. This paper...
Semantic concept learning is one of the most challenging problems in video retrieval. The key barrier for semantic concept learning is lack of annotated training data. Internet videos are different from ordinary videos: massive, rich information, customized, non-uniform format, uneven quality, little descriptive text, only a few shots with limited length etc. Therefore, Internet is a potential repository...
This article introduces ARUBAS, a new framework to build associative classifiers. In contrast with many existing associative classifiers, it uses class association rules to transform the feature space and uses instance-based reasoning to classify new instances. The framework allows the researcher to use any association rule mining algorithm to produce the class association rules. Every aspect of the...
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