Two learning approaches for a rule-based intuitive reasoner

摘要

This paper is a discussion of two continuous learning approaches for improving classification accuracy for an intuitive reasoner algorithm. The reasoner predicted the value of a given target variable by multiple iterations of forward-chained, rule-based inference. Each rule in the reasoner’s rule set had associated with it a weight, referred to here as “Strength of Belief” (SB). The value of SB of a rule indicated the certainty level of that rule. In each iteration of reasoning, any instances of similar values for a given variable were replaced by a single consolidated datum and the SB associated with the consolidated datum was increased. At the end of the reasoning process, the class (value) of the target variable which had the highest SB was reported as the conclusion. The rule set for the reasoner was generated based on a training data set that contained 80% of the data in a weather database comprising 50 years worth of hourly measurements for 54 weather variables. Each rule was induced based on only a small subset of the weather data. The intuitive reasoner was tested by using the induced rules to predict a number of pre-selected target variables using 275 test cases created from the test data. The first continuous learning approach was to identify relevant input variables for the reasoner, and the second was to rebalance the rule set used by the reasoner by adjusting the SB associated with each of the rules. Because of the way the rules were induced, the resulting rules did not contain any information about the relevance of the 53 possible input variables to the task of predicting a given target variable for previously unseen cases. A method was developed to identify which input variables were most relevant to the task based on the induced rule set. This method resulted in higher prediction accuracy of the intuitive reasoner than using a set of randomly chosen input variables for four of six target variables. The second continuous learning approach was intended to address the class imbalance problem in the rule set. The intuitive reasoner appeared to over-fit classes (values) which had frequent representation in the rule set. To address this problem, a heuristic was developed that generated adjustment factors for the SB values of the rules. The use of this heuristic improved the classification accuracy of the intuitive reasoner for four of the six target variables.