The two-volume set LNCS 12615 + 12616 constitutes the refereed proceedings of the 12th International Conference on Intelligent Human Computer Interaction, IHCI 2020, which took place in Daegu, South Korea, during November 24-26, 2020.The 75 full and 18 short papers included in these proceedings were carefully reviewed and selected from a total of 185 submissions. The papers were organized in topical sections named: cognitive modeling and system; biomedical signal processing and complex problem solving; natural language, speech, voice and study; algorithm and related applications; crowd sourcing and information analysis; intelligent usability and test system; assistive living; image processing and deep learning; and human-centered AI applications.

The two-volume set LNCS 12615 + 12616 constitutes the refereed proceedings of the 12th International Conference on Intelligent Human Computer Interaction, IHCI 2020, which took place in Daegu, South Korea, during November 24-26, 2020.The 75 full and 18 short papers included in these proceedings were carefully reviewed and selected from a total of 185 submissions. The papers were organized in topical sections named: cognitive modeling and systems; biomedical signal processing and complex problem solving; natural language, speech, voice and study; algorithms and related applications; crowd sourcing and information analysis; intelligent usability and test system; assistive living; image processing and deep learning; and human-centered AI applications.

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 classifiers by recursive application of weak learning algorithms. Experimental results on various benchmark data sets indicate that the proposed methodologies are useful in constructing simpler and more accurate classifiers.