False flag: An evolutionary false labeling approach for multilabel classification

Multilabel classification tackles scenarios where each sample concurrently belongs to multiple binary classes, referred to as labels. The task of learning multilabel datasets is harder than single-label classification. In addition to its inherent complexity, low-density label datasets, noisy labels, and complex relationships between labels make this problem extremely difficult. In this paper, we present a new approach, false flag labeling , which is based on modifying the labels of the instances of the training set to obtain a new training set that can improve the classification model’s performance. We assume that adding new relevant labels to the training set or removing relevant labels from it may improve the results of the learning algorithm, making the search for separation surfaces easier. We do not assume that the added labels correspond to the actual labels of the instances that were missed in the dataset labeling process, nor do we assume that the removed labels correspond to erroneously labeled instances. We use an evolutionary algorithm to approach the false labeling process as an optimization process. An extensive comparison using 50 datasets and seven classification models demonstrates the advantageous performance of our approach. • We present a new method for improving multi-label classification. • The method relies on modifying the training set’s labels to improve results. • The comparison uses four different metrics. • We study the method’s performance in the context of missing labels.