Classification Rule for Small Samples: A Bootstrap Approach
نویسندگان
چکیده
#1 Dept. of Statistics, Islamic University, Kushtia, Bangladesh, Phone no. +8801718698811 #2 Dept. of Statistics, Islamic University, Kushtia, Bangladesh, Phone no. +8801716657066 #3 Dept. of Statistics, Jahangirnagar University, Savar, Dhaka, Bangladesh, Phone no. +8801711145041 ABSTRACT In a recent year, classification is computer implemented and most popular data mining technique. Thus in this paper, we address the issue of classification errors over small samples and propose a new Bootstrap based approach for quantifying the level of classification errors. We investigate the performances of classification techniques and observed that, Bootstrap based classification techniques significantly reduce the classification errors than the usual techniques of small samples. Thus, this paper proposes to apply classification techniques under Bootstrap approach for classifying objects in case of small samples.
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In classification problems, there are several attempts to create rules which assign future observations to certain classes. Common methods are for example linear discriminant analysis or classification trees. Recent developments lead to substantial reduction of misclassification error in many applications. Bootstrap aggregation (“bagging”, Breiman, 1996a) combines classifiers trained on bootstr...
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