Searching for Credible Relations in Machine Learning

When machine learning (ML) and data mining (DM) methods construct models in complex domains, models can contain less-credible parts [2], which are statistically significant, but meaningless to the human analyst. For example, let us consider a decision tree model presented in Figure 1. The tree is constructed with the J48 algorithm in Weka [8] for a complex domain indicating which segments of research and development (R&D) sector have the highest impact on economic welfare of a country. Nodes in the tree represent segments of the R&D sector. Leaves in the tree represent economic welfare of the majority of countries that reached the specific leaf. Economic welfare can be: low, middle or high. In each leaf, the first number in brackets represents the number of countries that reached that leaf. The second number represents the number of countries in that leaf with the level of welfare different than the one represented by the leaf. The quantities are expressed in decimals to account for those countries with missing values for segments appearing in the tree. Note that the left subtree is omitted to simplify the example.