Class-selective Rejection Research Articles

Optimal Decision Rule with Class-Selective Rejection and Performance Constraints

The problem of defining a decision rule which takes into account performance constraints and class-selective rejection is formalized in a general framework. In the proposed formulation, the problem is defined using three kinds of criteria. The first is the cost to be minimized, which defines the objective function, the second are the decision options, determined by the admissible assignment classes or subsets of classes, and the third are the performance constraints. The optimal decision rule within the statistical decision theory framework is obtained by solving the stated optimization problem. Two examples are provided to illustrate the formulation and the decision rule is obtained.

Gene‐Based Multiclass Cancer Diagnosis with Class‐Selective Rejections

Supervised learning of microarray data is receiving much attention in recent years. Multiclass cancer diagnosis, based on selected gene profiles, are used as adjunct of clinical diagnosis. However, supervised diagnosis may hinder patient care, add expense or confound a result. To avoid this misleading, a multiclass cancer diagnosis with class-selective rejection is proposed. It rejects some patients from one, some, or all classes in order to ensure a higher reliability while reducing time and expense costs. Moreover, this classifier takes into account asymmetric penalties dependant on each class and on each wrong or partially correct decision. It is based on ν-1-SVM coupled with its regularization path and minimizes a general loss function defined in the class-selective rejection scheme. The state of art multiclass algorithms can be considered as a particular case of the proposed algorithm where the number of decisions is given by the classes and the loss function is defined by the Bayesian risk. Two experiments are carried out in the Bayesian and the class selective rejection frameworks. Five genes selected datasets are used to assess the performance of the proposed method. Results are discussed and accuracies are compared with those computed by the Naive Bayes, Nearest Neighbor, Linear Perceptron, Multilayer Perceptron, and Support Vector Machines classifiers.

Computational Data Mining in Cancer Bioinformatics and Cancer Epidemiology

Computational Data Mining in Cancer Bioinformatics and Cancer Epidemiology

CLASS-SELECTIVE REJECTION RULE TO MINIMIZE THE MAXIMUM DISTANCE BETWEEN SELECTED CLASSES

Class-selective rejection scheme assigns to an input pattern a subset of classes that are most likely to identify the pattern, and not simply reject it. Conventional class-selective rejection rules can minimize the error rate for a given average number of classes. In this paper, an example of unnatural classification is shown by using conventional rules and a new optimum criterion for establishing a class-selective rejection rule is proposed. Its optimality and upper-bound of error rate are proven. Finally, two examples are provided to illustrate various aspects of this optimum decision rule.

Optimum tradeoff between class-selective rejection error and average number of classes

The paper first reviews the recently proposed optimum class-selective rejection rule. This rule provides an optimum tradeoff between the error rate and the average number of (selected) classes. Then, a new general relation between the error rate and the average number of classes is presented. The error rate can be directly computed from the class-selective reject function, which in turn can be estimated from unlabelled patterns, by simply counting the rejected classes. Theoretical as well as practical implications are discussed, and some future research directions are proposed.

The optimum class-selective rejection rule

Class-selective rejection is an extension of simple rejection. That is, when an input pattern cannot be reliably assigned to one of the N classes in an N-class problem, it is assigned to a subset of classes that are most likely to issue the pattern, instead of simply being rejected. By selecting more classes, the risk of making an error can be reduced, at the price of subsequently having a larger remaining number of classes. The optimality of class-selective rejection is therefore defined as the best trade-off between error rate and average number of selected classes. Formally, the trade-off study is embedded in the framework of decision theory. The average expected loss is expressed as a linear combination of error rate and average number of classes. The minimization of the average expected loss, therefore, provides the best trade-off. The complexity of the resulting optimum rule is reduced, via a discrete convex minimization, to be linear in the number of classes. Upper-bounds on error rate and average number of classes are derived. An example is provided to illustrate various aspects of the optimum decision rule. Finally, the implications of the new decision rule are discussed.