Correct Classification Rates in Multi-Category Discriminant Analysis of Spatial Gaussian Data

This paper discusses the problem of classifying a multivariate Gaussian random field observation into one of the several categories specified by different parametric mean models. Investigation is conducted on the classifier based on plug-in Bayes classification rule (PBCR) formed by replacing unknown parameters in Bayes classification rule (BCR) with category parameters estimators. This is the extension of the previous one from the two category cases to the multi-category case. The novel closed-form expressions for the Bayes classification probability and actual correct classification rate associated with PBCR are derived. These correct classification rates are suggested as performance measures for the classifications procedure. An empirical study has been carried out to analyze the dependence of derived classification rates on category parameters.

Improved Collaborative Filtering Recommendation Based on Classification and User Trust

When dealing with the ratings from users,traditional collaborative filtering algorithms do not consider the credibility of rating data,which affects the accuracy of similarity.To address this issue,the paper proposes an improved algorithm based on classification and user trust.It firstly classifies all the ratings by the categories of items.And then,for each category,it evaluates the trustworthy degree of each user on the category and imposes the degree on the ratings of the user.Finally,the algorithm explores the similarities between users,finds the nearest neighbors,and makes recommendations within each category.Simulations show that the improved algorithm outperforms the traditional collaborative filtering algorithms and enhances the accuracy of recommendation.

Multicategory Classification Via Forward-Backward Support Vector Machine

In this paper,we propose a new algorithm to extend support vector machine(SVM)for binary classification to multicategory classification.The proposed method is based on a sequential binary classification algorithm.We first classify a target class by excluding the possibility of labeling as any other classes using a forward step of sequential SVM;we then exclude the already classified classes and repeat the same procedure for the remaining classes in a backward step.The proposed algorithm relies on SVM for each binary classification and utilizes only feasible data in each step;therefore,the method guarantees convergence and entails light computational burden.We prove Fisher consistency of the proposed forward–backward SVM(FB-SVM)and obtain a stochastic bound for the predicted misclassification rate.We conduct extensive simulations and analyze real-world data to demonstrate the superior performance of FB-SVM,for example,FB-SVM achieves a classification accuracy much higher than the current standard for predicting conversion from mild cognitive impairment to Alzheimer’s disease.