1. A method for defect classification comprising: storing, by a processor, a plurality of definitions of a plurality of defect classes in terms of a plurality of classification rules in a multi-dimensional feature space, wherein the plurality of classification rules, for each given defect class, defines in a feature space a boundary of a region associated with the given class and provides a confidence measure associated with classification of a defect to the given defect class, the confidence measure being indicative of a level of confidence as a function of the location of the defect in the feature space with respect to the respective boundaries; receiving, by the processor, inspection data associated with a plurality of defects detected in one or more samples under inspection; receiving, by the processor and from an operator, a classification performance measure selected from a plurality of performance measures, wherein the plurality of performance measures comprises at least one of a maximum rejection rate or a target purity level; determining at least one confidence threshold corresponding to the classification performance measure; applying, by the processor, an automatic classifier to the inspection data, the automatic classifier based on the plurality of definitions, and identifying a plurality of defects each classified with a low level of confidence based on the at least one confidence threshold and indicative of the defect being located in an overlap region between the respective boundaries of at least two of the defect classes; generating, by the processor, a plurality of classification results by applying, to the identified plurality of defects classified with the low level of confidence, at least one inspection modality that is different than the automatic classifier to assign each of the identified plurality of defects to one of the at least two of the defect classes associated with the overlap region; refining, by the processor, the automatic classifier to adjust boundaries of one or more defect classes of the plurality of classes when a threshold amount of the identified plurality of defects located in the overlap region have been classified by the at least one inspection modality that is different than the automatic classifier, wherein the refining is provided by training the automatic classifier using each classification result of the plurality of classification results of the identified plurality of defects classified with the low level of confidence.