Class probability distribution based maximum entropy model for classification of datasets with sparse instances

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.