Enhancing Multi-Label Classification Through Deep Extra-Trees and Transformation Techniques
Abdelkader Berrouachedi, Rakia Jaziri, Gilles Bernard · 2024
Multi-label classification presents a complex computational challenge with broad applications in text categorization, image annotation, and bioinformatics. In this paper, we introduce a pioneering approach that merges Deep Extra-Trees with three transformation methods to tackle this intricate task. Through comprehensive evaluations conducted on a range of benchmark datasets, we meticulously compare our method against established algorithms. The results not only validate our approach but also reveal its superiority, demonstrating enhanced performance and robustness. Our approach utilizes transformation methods of multi-class classification in conjunction with Deep Extra-Trees. Specifically, we implement Binary Relevance, Classifier Chains, and Label Powerset as our transformation methods, which effectively convert the multi-label problem into multiple single-label problems, thereby leveraging the power of Deep Extra-Trees for improved prediction accuracy. This substantiates the robustness and adaptability of our proposed methodology across diverse datasets. By offering a compelling solution to the multi-label classification problem, our research contributes significantly to the advancement of machine learning techniques in various domains. Moreover, we apply this approach not only to classification tasks but also to anomaly detection, further demonstrating its versatility and practical utility.