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In rough set approaches, decision rules are induced from a given data table showing the relation between attribute values and classes of objects. The induced decision rules are used for the classification of new objects by their attribute values. However, some of new objects do not match any decision rule conditions because the given data table does not always include all possible patterns. In those...
This paper proposes and implements robust optimization methodologies for making investment decisions in the capacity expansion planning (CEP) of power systems in an uncertain environment. Uncertainties of fuel prices, demand, and transmission capacity are captured in an uncertainty set. With adjustable robust optimization (ARO), we represent all the decision variables as affine functions of multiple...
This paper is about fault diagnosis rules. Firstly, a redundancy-based rule model is presented, compared with the reduced rule model without redundancy by Rough Set Theory (RST). This model has a robusticity to resist the data loss by sensor failure, as well as the inaccuracy data by sensor error. Furthermore, considering the constraint of fault diagnosis cost, a dynamic optimization method on the...
This paper proposes a novel voice activity detection (VAD) algorithm to improve the speech detection robustness in noisy environments. In the proposed algorithm, two-stage mel-warped Wiener filter is introduced to improve the performance of voice activity detector based on spectral entropy. Then an improved decision rule based on spectral entropy was derived. We have evaluated system performance under...
Interactive image search or relevance feedback is the process which helps a user refining his query and finding difficult target categories. This consists in partially labeling a very small fraction of an image database and iteratively refining a decision rule using both the labeled and unlabeled data. Training of this decision rule is referred to as transductive learning. Our work is an original...
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