Due to the digital revolution, the amount of data to be processed is growing
every day. One of the more common functions used to process these data is
classification. However, the results obtained by most existing classifiers
are not satisfactory, as they often depend on the number and type of
attributes within the datasets. In this paper, a maximum entropy model based
on class probability distribution is proposed for classifying data in sparse
datasets with fewer attributes and instances. Moreover, a new idea of using
Lagrange multipliers is suggested for estimating class probabilities in the
process of class label prediction. Experimental analysis indicates that the
proposed model has an average accuracy of 89.9% and 86.93% with 17 and 36
datasets. Besides, statistical analysis of the results indicates that the
proposed model offers greater classification accuracy for over 50% of
datasets with fewer attributes and instances than other competitors.
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