We present an efficient random decision tree algorithm for case-based reasoning systems. We combine this algorithm with a simple similarity measure based on domain knowledge to create a stronger hybrid algorithm. This combination is based on our general approach for combining lazy and eager learning methods. We evaluate the resulting algorithms on a case base of patient records in a palliative care domain. Our hybrid algorithm consistently produces a lower average error than the base algorithms.
This volume contains the papers presented at the 24th International FLAIRS Conference (FLAIRS-24) held 18–20 May 2011 in Palm Beach, Florida, USA. The call for papers attracted 179 submissions, across 13 different tracks. Special tracks are a vital part of theF LAIRS conferences, with 12 held at FLAIRS-24. Over 90 percent of the papers were reviewed by four or more reviewers, and all papers were reviewed by at least three. These reviews were coordinated by the program committees of the general conference and the special tracks. The accepted submissions include 94 papers and 37 poster papers that appear in these proceedings.
In this paper, we show how to make a cognitive tutoring agent capable of precise causal reasoning by integrating constraints with data mining algorithms. Putting constraints on recorded interactions between the agent and learners during learning activities allows data mining algorithms to extract the causes of the learners' problems. Subsequently, the agent uses this information to provide useful and customized explanations to learners.