Multi-class classification using a new Bayesian method

TL;DR: A new Bayesian method for multi-class classification is proposed. The model incorporates k-means algorithm for prior probability estimation, kernel function for probability density function estimation, and multi-class classification capabilities. Experimental results demonstrate the effectiveness of the model on image and fire data sets.

Abstract: This paper proposes a new classification model using the Bayes method. This model not only determines the prior probability based on the k-means algorithm, builds the method for estimating the probability density function via the kernel function, but also classifies the objects to the known populations. The proposed model is described via the experiment of image classifying. In this example, we first use the Gray level co-occurrence matrix to extract the features of images, and next classify this data set based on the improved Bayesian method. In another application, we also build the classification problem for the Algerian Forest Fires data set. The outstanding advantages of this method are the adaptive ability of the kernel function, the classification for multi-class, and the reduction of computational costs. In addition, the experimental results also show the potential of the developed model.