Abstract:
The data classification is the main analyzing method for the information
extracting. Classification in data mining has gained a lot of importance in literature
and it has a great deal of application areas from medicine to astronomy, from banking
to text classification, and so on. It can be described as supervised learning algorithm
as it assigns class labels to data objects based on the relationship between the data
items with a pre-defined class label. The classification techniques are help to learn a
model from a set of training data and to classify a test data well into one of the
classes.
This dissertation builds upon the ideas introduced work by Vladimir N. Vapnik,
Y. J. Lee and O. L. Mangasarian in Support Vector Machine for data Classification
Problems. This research is related to the study of the existing SVM classification
algorithm and new scheme that reduces the complexity of SVM. The proposed
scheme describes the new scheme based on computational reduction in support vector
machine that provides reasonably increase the evaluation speed without affecting the
performance of the standard support vector machine. It helps other researchers in
studying the existing algorithms as well as developing innovative algorithms for
applications or requirements which are not available.
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From the results, we conclude that with the help of proposed RSV approach, no.
of support vectors for temporal dataset reduces compared to standard SVM. Hence it
gives faster execution and better performance for real time large databases
applications. Therefore Efficiency of SVM algorithm should also increase in case of
temporal dataset analysis compared to standard SVM.
