Bapu B. Kiranagi

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In this paper, a simple and efficient feature selection scheme for symbolic data is proposed. The proposed scheme exploits the symbolic multivalued proximity measures for feature selection. The effectiveness of the proposed scheme has been demonstrated through experiments on standard symbolic data sets
In this paper, a simple and efficient symbolic text classification is presented. A text document is represented by the use of interval valued symbolic features. Subsequently, a new feature selection method based on a new dissimilarity measure is also presented. The new feature selection method reduces the features in the representation phase for effective(More)
In this paper, we bring out the importance of nonsymmetric proximity values among symbolic objects in simulating the reality during clustering. The concept of Mutual Neighborhood Value (MNV) has been exploited on non-symmetric proximity values. The results of the experiments conducted reveal that the approaches based on non-symmetric proximity measures best(More)
In this paper, we present a novel approach for higher level semantic video archival and retrieval based on symbolic representation. Topological triangular spatial relationship has been introduced to study the higher level relationship (spatial and topological) among the objects in a frame. A two dimensional time series representation to preserve the(More)
In this paper a new statistical measure for estimating the degree of dissimilarity between two symbolic objects whose features are multivalued symbolic data type is proposed. In addition two new simple representation techniques viz., interval type and magnitude type for the computed dissimilarity between the symbolic objects are introduced. The(More)
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