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One-Class-At-A-Time Removal Sequence Planning Method For Multiclass Classification Problems, Chieh-Neng Young, Chen-Wen Yen, Yi-Hua Pao, Mark L. Nagurka
One-Class-At-A-Time Removal Sequence Planning Method For Multiclass Classification Problems, Chieh-Neng Young, Chen-Wen Yen, Yi-Hua Pao, Mark L. Nagurka
Mechanical Engineering Faculty Research and Publications
Using dynamic programming, this work develops a one-class-at-a-time removal sequence planning method to decompose a multiclass classification problem into a series of two-class problems. Compared with previous decomposition methods, the approach has the following distinct features. First, under the one-class-at-a-time framework, the approach guarantees the optimality of the decomposition. Second, for a K-class problem, the number of binary classifiers required by the method is only K-1. Third, to achieve higher classification accuracy, the approach can easily be adapted to form a committee machine. A drawback of the approach is that its computational burden increases rapidly with the number of classes. …
Building Urban Texture, Fernando Pabon-Rico
Building Urban Texture, Fernando Pabon-Rico
Architecture Senior Theses
"This investigation is based on the contention that a city is made up of an urban construct where certain elements are continuous in their presence. This continuity provides legibility to an urban environment. The fact that elements or parts are an integral part of the legibility of the city fosters the reading of the city as a function of their integration. The role or place of architecture therefore lies on the formation of the city through its parts."
Comparisons Of K-Anonymization And Randomization Schemes Under Linking Attacks, Zhouxuan Teng, Wenliang Du
Comparisons Of K-Anonymization And Randomization Schemes Under Linking Attacks, Zhouxuan Teng, Wenliang Du
Electrical Engineering and Computer Science - All Scholarship
Recently K-anonymity has gained popularity as a privacy quantification against linking attacks, in which attackers try to identify a record with values of some identifying attributes. If attacks succeed, the identity of the record will be revealed and potential confidential information contained in other attributes of the record will be disclosed. Kanonymity counters this attack by requiring that each record must be indistinguishable from at least K − 1 other records with respect to the identifying attributes. Randomization can also be used for protection against linking attacks. In this paper, we compare the performance of K-anonymization and randomization schemes under …