On the Construction of Multi-Relational Classifier Based on Canonical Correlation Analysis
Raziyeh Zall, Mohammad Reza Kangavari · 2019
In this paper, we introduce MRMDCCA for classifying multi relational data. Multi-relational data are stored on relational databases where they consist of multiple relations that are linked together by entity-relationship links. MRMDCCA takes advantage of correlation information of related relations to predict the class label. The proposed approach creates two different multiple feature sets, multiple feature sets based on propagating label information and multiple fused feature sets based on extracting correlation information. It propagates labels from the target table to the background tables based on foreign key paths to create multiple feature sets based on propagating label information. It proposes a approach based on Canonical correlation analysis (CCA) to extracting correlation information between related tables based on join paths to create multiple fused feature sets based on extracting correlation information. Finally, it applies traditional classifiers on two created feature sets and combines result of classifiers by using meta-learner. Testing has been performed on two diverse datasets. We compare our proposed classifier with other state-of-the-art multi relational classifiers which use different approaches to deal with multi relational setting. We showed that the proposed classifier achieves promising results in experiments.