Abstract—Bagging is commonly used to improve the
performance of a classification algorithm by first using
bootstrap sampling on the given data set to train a number of
classifiers and then using the majority voting mechanism to
aggregate their outputs. However, the improvement would be
limited in the situation where the given data set contains
missing values and the algorithm used to train the classifiers is
sensitive to missing values. We propose an extension of bagging
that considers not only the weights of the classifiers in the voting
process but also the incompleteness of the bootstrapped data
sets used to train the classifiers. The proposed extension assigns
a weight to each of the classifiers according to its classification
performance and adjusts the weight of each of the classifiers
according to the ratio of missing values in the data set on which
it is trained. In experiments, we use two classification
algorithms, two measures for weight assignment, and two
functions for weight adjustment. The results reveal the potential
of the proposed extension of bagging for working with
classification algorithms sensitive to missing values to perform
classification on data sets having small numbers of instances but
containing relatively large numbers of missing values.
Index Terms—Bagging, missing values, multilayer perceptron, sequential minimal optimization.
Kuo-Wei Hsu is with the Department of Computer Science, National Chengchi University, Taipei, Taiwan (e-mail: email@example.com).
Cite:Kuo-Wei Hsu, "Weight-Adjusted Bagging of Classification Algorithms Sensitive to Missing Values," International Journal of Information and Education Technology vol. 3, no. 5, pp. 560-566, 2013.