Interval SVM-Based Classification Algorithm Using the Uncertainty Trick

A new robust SVM-based algorithm of the binary classification is proposed. It is based on the so-called uncertainty trick when training data with the interval uncertainty are transformed to training data with the weight or probabilistic uncertainty. Every interval is replaced by a set of training points with the same class label such that every point inside the interval has an unknown weight from a predefined set of weights. The robust strategy dealing with the upper bound of the interval-valued expected risk produced by a set of weights is used in the SVM. An extension of the algorithm based on using the imprecise Dirichlet model is proposed for its additional robustification. Numerical examples with synthetic and real interval-valued training data illustrate the proposed algorithm and its extension.

Extensions of Bayesian Reliability Analysis by Using Imprecise Dirichlet Model

Bayesian approaches have been demonstrated as effective methods for reliability analysis of complex systems with small-amount data, which integrate prior information and sample data using Bayes’ theorem. However, there is an assumption that precise prior probability distributions are available for unknown parameters, yet these prior distributions are sometimes unavailable in practical engineering. A possible way to avoiding this assumption is to generalize Bayesian reliability analysis approach by using imprecise probability theory. In this paper, we adopt a set of imprecise Dirichlet distributions as priors to quantify uncertainty of unknown parameters and extend traditional Bayesian reliability analysis approach by introducing an imprecise Dirichlet model (IDM). When the prior information is rare, the result of imprecise Bayesian analysis method is too rough to support engineering decision-making, so we proposed an optimization model to reduce the imprecision of the new method. Spindles are crucial for machine tools and reliability data related to spindles of new-developed machine tools are often rare. We can then use the imprecise Bayesian reliability analysis method to assess its reliability. In this paper, we mainly investigate the reliability assessment of a motorized spindle to illustrate the effectiveness of the proposed method.

IMPRECISE CLASSIFICATION WITH CREDAL DECISION TREES

In this paper, we present the following contributions: (i) an adaptation of a precise classifier to work on imprecise classification for cost-sensitive problems; (ii) a new measure to check the performance of an imprecise classifier. The imprecise classifier is based on a method to build simple decision trees that we have modified for imprecise classification. It uses the Imprecise Dirichlet Model (IDM) to represent information, with the upper entropy as a tool for splitting. Our new measure to compare imprecise classifiers takes errors into account. Thus far, this has not been considered by other measures for classifiers of this type. This measure penalizes wrong predictions using a cost matrix of the errors, given by an expert; and it quantifies the success of an imprecise classifier based on the cardinal number of the set of non-dominated states returned. To compare the performance of our imprecise classification method and the new measure, we have used a second imprecise classifier known as Naive Credal Classifier (NCC) which is a variation of the classic Naive Bayes using the IDM; and a known measure for imprecise classification.