Inductive learning performance changing with relevant inputs

Abstract

One of the difficulties in using the current information retrieval systems is that it is hard for a user, especially a novice, to formulate a query effectively. One solution to this problem is to automate the process of query reformulation using the relevance feedback from the previous search. In this research, a Boolean query is viewed as a classifier and a decision tree classifier (ID3) is revised to act as a query in information retrieval (call it ID3-IR). The current emphasis in our experiments is to analyze the changes in the retrieval performance (measured by recall, precision, and E) of the ID3-IR using a different number of relevant input documents. Based on the test set, MEDLARS, it is shown that an input set with more relevant documents achieves higher recall and lower precision. In overall performance analysis measured by E, an input set with more relevant documents is superior to one with less relevant documents after the second reformulation.