In a typical inductive learning scenario, instances in a data set are simply represented as ordered tuples of attribute values. In my research, I explore three methodologies to improve the accuracy and compactness of the classifiers: abstraction, aggregation, and recursion. Firstly, abstraction is aimed at the design and analysis of algorithms that generate and deal with taxonomies for the construction of compact and robust classifiers. Secondly, I apply aggregation method to constructively invent features in a multiset representation for classification tasks. Finally, I construct a set of...
In a typical inductive learning scenario, instances in a data set are simply represented as ordered tuples of attribute values. In my research, I expl...
Dae-Ki Kang Rayner Alfred Zamhar Iswandono Bin Awang Ismail
This book gathers the proceedings of the 9th International Conference on Computational Science and Technology (ICCST 2022), held in Johor Bahru, Malaysia, on August 27–28, 2022. The respective contributions offer practitioners and researchers a range of new computational techniques and solutions, identify emerging issues, and outline future research directions, while also showing them how to apply the latest large-scale, high-performance computational methods.
This book gathers the proceedings of the 9th International Conference on Computational Science and Technology (ICCST 2022), held in Johor Bahru, Malay...