table confusion matrix for authorized personnel classificationusing different classifier (classifier predicted actual authorized non-authorized authorized non-authorized , table comparing the metrics for the confusion matrices in tables and metric multilabel classification hierarchical classification classifier classifier accuracy - precision - recall - specificity - - - - - - - remark (multilabel and hierarchical classificationin standard classification the classes are assumed to be mutually exclusive for example satellite image could be classified as "cloudy""clear"or "foggyin multilabel classification the classes (often called labelsdo not have to be mutually exclusive in this case the response is subset of some collection of labels { equivalentlythe response can be viewed as binary vector of length cwhere the -th element is if the response belongs to label and otherwise againconsider the satellite image example and add two labelssuch as "roadand "riverto the previous three labels clearlyan image can contain both road and river in additionthe image can be clearcloudyor foggy in hierarchical classification hierarchical relation between classes/labels is taken into account during the classification process usuallythe relations are modeled via tree or directed acyclic graph visual comparison between the hierarchical and non-hierarchical (flatclassification tasks for satellite image data is presented in figure root root rural urban farm barn skyscraper rural barn farm urban skyscraper figure hierarchical (leftand non-hierarchical (rightclassification schemes barns and farms are common in rural areaswhile skyscrapers are generally located in cities while this relation can be clearly observed in the hierarchical model schemethe connection is missing in the non-hierarchical design exact match ratio : (xand the true response are in multilabel classificationboth the prediction subsets of the label set { - reasonable metric is the so-called exact match ratio