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- W2891332346 abstract "Support Vector Machine (SVM) is on one of its kind of data mining algorithm, tends to always give away the global optimum solution to any given problem because of its convex optimization problem solving approach. SVM learns from the given training data and generates a linear hyperplane classifying the given instances. The training instances which are near this separating hyperplane are called support vectors, using which predictions are obtained. In other words, SVM considers the support vectors as important instances for prediction purposes and ignores other instances which are much away from hyperplane boundary. In this project the ability of SVM i.e. extraction of support vectors is explored further and active learning based synthetic data generation (nearest to the support vectors) is employed. Synthetic data generation near support vectors would provide the classification algorithm (Naive Bayes algorithm in this project) with some extra and suitable instances to learn from which in-turn expected to result in improving the prediction accuracy of the classifier used. Results have shown that Naive Bayes classifier yielded superior accuracy using the modified dataset when compared to the accuracy yielded using original data available." @default.
- W2891332346 created "2018-09-27" @default.
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- W2891332346 date "2017-09-01" @default.
- W2891332346 modified "2023-09-26" @default.
- W2891332346 title "Data Classification using Active Learning based Data Modification: An Application to Churn Prediction" @default.
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- W2891332346 doi "https://doi.org/10.1109/ctceec.2017.8454989" @default.
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