Open AccessSemi-supervised learning with the clustering and Decision Trees classifier for the task of cognitive workload study
Author(s) -
Martyna Wawrzyk
Publication year - 2020
Publication title -
journal of computer sciences institute
Language(s) - English
Resource type - Journals
ISSN - 2544-0764
DOI - 10.35784/jcsi.1725
Subject(s) - cluster analysis , workload , artificial intelligence , computer science , classifier (uml) , pattern recognition (psychology) , decision tree , cognition , machine learning , data mining , psychology , neuroscience , operating system
The paper is focused on application of the clustering algorithm and Decision Tress classifier (DTs) as a semi-supervised method for the task of cognitive workload level classification. The analyzed data were collected during examination of Digit Symbol Substitution Test (DSST) with use of eye-tracker device. 26 participants took part in examination as volunteers. There were conducted three parts of DSST test with different levels of difficulty. As a results there were obtained three versions of data: low, middle and high level of cognitive workload. The case study covered clustering of collected data by using k-means algorithm to detect three clusters or more. The obtained clusters were evaluated by three internal indices to measure the quality of clustering. The David-Boudin index detected the best results in case of four clusters. Based on this information it is possible to formulate the hypothesis of the existence of four clusters. The obtained clusters were adopted as classes in supervised learning and have been subjected to classification. The DTs was applied in classification. There were obtained the 0.85 mean accuracy for three-class classification and 0.73 mean accuracy for four-class classification.