Computing and Information Systems - Research Publications
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ItemA Weighting Scheme Based on Emerging Patterns for Weighted Support Vector Machines( 2005)Support Vector Machines (SVMs) are powerful tools for solving classification problems and have been applied to many application fields, such as pattern recognition and data mining, in the past decade. Weighted Support Vector Machines (weighted SVMs) extend SVMs by considering that different input vectors make different contributions to the learning of decision surface. An important issue in training weighted SVMs is how to develop a reliable weighting model to reflect the true noise distribution in the training data, i.e., noise and outliers should have low weights. In this paper we propose to use Emerging Patterns (EPs) to construct such a model. EPs are those itemsets whose supports in one class are significantly higher than their supports in the other class. Since EPs of a given class represent the discriminating knowledge unique to their home class, noise and outliers should contain no EPs or EPs of the both contradicting classes, while a representative instance of the class should contain strong EPs of the same class. We calculate numeric scores for each instance based on EPs, and then assign weights to the training data using those scores. An extensive experiments carried out on a large number of benchmark datasets show that our weighting scheme often improves the performance of weighted SVMs over SVMs. We argue that the improvement is due to the ability of our model to approximate the true distribution of data points.
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ItemExpanding the Training Data Space Using Emerging Patterns and Genetic Methods(Society for Industrial and Applied Mathematics, 2005)
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ItemBroadening vector space schemes for improving the quality of information retrieval(SPRINGER-VERLAG BERLIN, 2005)
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ItemA novel document ranking method using the discrete cosine transform(IEEE COMPUTER SOC, 2005-01)We propose a new Spectral text retrieval method using the Discrete Cosine Transform (DCT). By taking advantage of the properties of the DCT and by employing the fast query and compression techniques found in vector space methods (VSM), we show that we can process queries as fast as VSM and achieve a much higher precision.
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ItemIncremental maintenance of shortest distance and transitive closure in first-order logic and SQL(ASSOC COMPUTING MACHINERY, 2005-09)Given a database, the view maintenance problem is concerned with the efficient computation of the new contents of a given view when updates to the database happen. We consider the view maintenance problem for the situation when the database contains a weighted graph and the view is either the transitive closure or the answer to the all-pairs shortest-distance problem ( APSD ). We give incremental algorithms for APSD , which support both edge insertions and deletions. For transitive closure, the algorithm is applicable to a more general class of graphs than those previously explored. Our algorithms use first-order queries, along with addition (+) and less-than (<) operations ( FO (+,<)); they store O ( n 2 ) number of tuples, where n is the number of vertices, and have AC 0 data complexity for integer weights. Since FO (+,<) is a sublanguage of SQL and is supported by almost all current database systems, our maintenance algorithms are more appropriate for database applications than nondatabase query types of maintenance algorithms.