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We address the problem of automatically acquiring case frame patterns from large corpus data. In particular, we view this problem as the problem of estimating a (conditional) distribution over a partition of words, and propose a new generalization method based on the MDL (Minimum Description Length) principle. In order to assist with the ef-ciency, our(More)
We propose and evaluate a family of methods for converting classifier learning algorithms and classification theory into cost-sensitive algorithms and theory. The proposed conversion is based on cost-proportionate weighting of the training examples, which can be realized either by feeding the weights to the classification algorithm (as often done in(More)
Cost-sensitive learning addresses the issue of classification in the presence of varying costs associated with different types of misclassification. In this paper, we present a method for solving multi-class cost-sensitive learning problems using any binary classification algorithm. This algorithm is derived using hree key ideas: 1) iterative weighting; 2)(More)
Recently, there has been increasing interest in the issues of cost-sensitive learning and decision making in a variety of applications of data mining. A number of approaches have been developed that are effective at optimizing cost-sensitive decisions when each decision is considered in isolation. However, the issue of sequential decision making, with the(More)
Attribution of climate change to causal factors has been based predominantly on simulations using physical climate models, which have inherent limitations in describing such a complex and chaotic system. We propose an alternative, data centric, approach that relies on actual measurements of climate observations and human and natural forcing factors.(More)