Concise and unambiguous assessment of a machine learning algorithm is key to classifier design and performance improvement. In the multi-class classification task, where each instance can only be labeled as one class, the confusion matrix is a powerful tool for performance assessment by quantifying the classification overlap. However, in the multi-label classification task, where each instance can be labeled with more than one class, the confusion matrix is undefined. Performance assessment of the multi-label classifier is currently based on calculating performance averages, such as hamming loss, precision, recall, and F-score. While the current assessment techniques present a reasonable representation of each class and overall performance, their aggregate nature results in ambiguity when identifying false negative ( FN ) and false positive ( FP ) results. To address this gap, we define a method of creating the multi-label confusion matrix (MLCM) based on three proposed categories of multi-label problems. After establishing the shortcomings of current methods for identifying FN and FP , we demonstrate the usage of the MLCM with the classification of two publicly available multi-label data sets: i) a 12-lead ECG data set with nine classes, and ii) a movie poster data set with eighteen classes. A comparison of the MLCM results against statistics from the current techniques is presented to show the effectiveness in providing a concise and unambiguous understanding of a multi-label classifier behavior.

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