Classification of the difficulty of a climbing route using the transformation of representation spaces and cascading classifier ensemble (CCE)

Climbing has gained popularity in recent years and encompasses various disciplines, among which bouldering stands out as one of the most well-known. Determining the difficulty of a bouldering route is a challenging task due to the varying combinations of handholds. Accurate assessment of the difficulty of a route is important, as it allows a climber’s ability to be measured against that of other climbers. Furthermore, climbers must gradually progress from less to more difficult routes to improve their skills effectively. The difficulty rating of a route poses a complex problem, and previous research has explored machine learning techniques, including deep learning, to address it. We propose a novel approach based on a series of transformations in the representation space that incorporates expert knowledge to improve the classification of climbing route difficulty. We introduce a cascade classifier ensemble (CCE) model specifically designed for levelling classification. Compared to similar works, this model also incorporates explainable artificial intelligence (XAI) mechanisms. With the CEE model, the relevance of specific handholds in the transition from one level of difficulty to the next is obtained. Finally, we illustrate how our proposed model not only achieves good results but also empowers climbers to identify the most crucial sections in each level of difficulty. • The transformative pipeline includes expert knowledge, enhancing classifier performance. • CCE excels in classifying climbing route difficulty. • CCE identifies key handholds to level up.