For example, suppose we have four solutions in Q as shown in Table 2. Each solution has a label and a membership degree with respect to the positive class predicted by the classifier. When the accuracy of the classifier is larger than or equal to 70%, these solutions are ranked in descending order with respect to their membership degrees (i.e., s2 > s3 > s1 > s4). When the accuracy of the classifier is larger than or equal to 30% and smaller than 70%, the positive solutions are ranked in ascending order with respect to their membership degrees (i.e., s3 > s2). Next, the negative solutions are ranked in descending order with respect to their membership degrees (i.e., s1 > s4). Then, the positive solutions are ranked before the negative solutions (i.e., s3 > s2 > s1 > s4). When the accuracy of the classifier is smaller than 30%, these solutions are ranked in ascending order with respect to their membership degrees (i.e., s4 > s1 > s3 > s2).

这段文字描述了一个在分类器准确率不同范围内如何对解决方案进行排名的过程。其中，每个解决方案都有一个标签和一个属于正类的隶属度。当分类器准确率大于等于70%时，这些解决方案按照隶属度从大到小排名。当分类器准确率大于等于30%且小于70%时，正类解决方案按照隶属度从小到大排名，负类解决方案按照隶属度从大到小排名。然后，正类解决方案排在负类解决方案前面。当分类器准确率小于30%时，这些解决方案按照隶属度从小到大排名。