Learning from multiple data sets with different missing attributes and privacy policies: Parallel distributed fuzzy genetics-based machine learning approach

This paper discusses parallel distributed genetics-based machine learning (GBML) of fuzzy rule-based classifiers from multiple data sets. We assume that each data set has a similar but different set of attributes. In other words, each data set has different missing attributes. Our task is the design of a fuzzy rule-based classifier from those data sets. In this paper, we first show that fuzzy rules can handle missing attributes easily. Next we explain how parallel distributed fuzzy GBML can handle multiple data sets with different missing attributes. Then we examine the accuracy of obtained fuzzy rule-based classifiers from various settings of available training data such as a single data set with no missing attribute and multiple data sets with many missing attributes. Experimental results show that the use of multiple data sets often increases the accuracy of obtained fuzzy rule-based classifiers even when they have missing attributes. We also discuss the learning from a data set under a severe privacy preserving policy where only the error rate of each candidate classifier is available. It is assumed that no information about each individual pattern is available. This means that we cannot use any information on the class label or the attribute values of each pattern. We explain how such a black-box data set can be utilized for classifier design.