A Hierarchical Ensemble of Decision Trees Applied to Classifying Data from a Psychological Experiment
Yannick Lallement · 1998
Classifying by hand complex data coming from psychology experiments can be a long and difficult task, because of the quantity of data to classify and the amount of training it may require. One way to alleviate this problem is to use machine learning techniques. We built a classifier based on decision trees that reproduces the classifying process used by two humans on a sample of data and that learns how to classify unseen data. The automatic classifier proved to be more accurate, more constant and much faster than classification by hand. Introduction Classification of complex data coming from psychological experiments is an important issue in cognitive psychology. Such classification is often done by two or more persons who subjectively rate the human behavior. This process can be long and labor-intensive, and there is often too much data to be classified by humans in a reasonable amount of time. Moreover, the psychology community considers the classification acceptable if the inter-r...