  Imbalanced dataset is occurred due to uneven distribution of data available
in the real world such as disposition of complaints on government offices in
Bandung. Consequently, multi-label text categorization algorithms may not
produce the best performance because classifiers tend to be weighed down by the
majority of the data and ignore the minority. In this paper, Bagging and
Adaptive Boosting algorithms are employed to handle the issue and improve the
performance of text categorization. The result is evaluated with four
evaluation metrics such as hamming loss, subset accuracy, example-based
accuracy and micro-averaged f-measure. Bagging ML-LP with SMO weak classifier
is the best performer in terms of subset accuracy and example-based accuracy.
Bagging ML-BR with SMO weak classifier has the best micro-averaged f-measure
among all. In other hand, AdaBoost MH with J48 weak classifier has the lowest
hamming loss value. Thus, both algorithms have high potential in boosting the
performance of text categorization, but only for certain weak classifiers.
However, bagging has more potential than adaptive boosting in increasing the
accuracy of minority labels.
