Abstract
The classification of tourism attractions is becoming a more well admirable research trend. Various techniques
have been taken on board to classify tourism attractions to provide information to the users based on their
preferences. This study provides an in-depth insight into the entropic distance-based classification approach for the
prediction of user attractions. In this study, a lazy classification technique, k-star, is implemented to predict the
tourism places based on user ratings. The k-star algorithm is the nearest neighbor approach that discovers the
nearest instances to the targeted instance. Unlike other nearest neighbor approaches, the k-star algorithm exploits
entropic distance, which measures all the possible shortest paths to discover the nearest instances based on user
ratings. Furthermore, the evaluation assessments are also carried out to justify the performance of the k-star
algorithm.
Naveed Jhamat, Hafiz Amaad, Ghulam Mustafa, Naeem A. Nawaz, Muzaffar Bashir, Muhammad Atif Makhdoom. (2021) Entropic Distance-based Classification of Tourist Attractions Using K* Learner: An Instance-based Approach, Journal of the Research Society of Pakistan, Volume-58, Issue-1.
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