Incremental transfer RULES with incomplete data

Recently strong AI emerged from artificial intelligence due to need for a thinking machine. In this domain, it is necessary to deal with dynamic incomplete data and understanding of how machines make their decision is also important, especially in information system domain. One type of learning called Covering Algorithms (CA) can be used instead of the difficult statistical machine learning methods to produce simple rule with powerful prediction ability. However, although using CA as the base of strong AI is a novel idea, doing so with the current methods available is not possible. Thus, this paper presents a novel CA (RULES-IT) and tests its performance over incomplete data. This algorithm is the first incremental algorithm in its family, and CA as a whole, that transfer rules from different domains and introduce intelligent aspects using simple representation. The performance of RULES-IT will be tested over incomplete and complete data along with other algorithms in the literature. It will be validated using 5-fold cross validation in addition to Friedman with Nemenyi post hoc tests to measure the significance and rank the algorithms.