Improving efficiency of heterogeneous multi relational classification by choosing efficient classifiers using ratio of success rate and time. Issue 1 (2nd January 2017) - Thakkar, Amit; Kosta, Y. P.
Abstract: Traditional data mining algorithms will not work efficiently for most of the real world applications where the data is stored in relational format. Useful patterns can certainly be extracted from multiple relations using an existing traditional learning algorithm of data mining, but it would involve a lot of complexity. So there is a need of a multi relational classification, which analyzes relational data and predicts unknown patterns automatically. Moreover the performances of existing relational classifiers are limited, because the existing algorithms are not able to use different classifiers based on characteristics of different relations. The goal of the proposed approach is to select appropriate classifiers based on characteristics of different relations in the relational database to improve the overall performance without affecting the running time. So multi criteria classifier selection function based on ratio of accuracy and running time is used to select the most efficient classifier using Meta Learning. In the proposed classifier selection function, accuracy is used as a measure of benefit and running time is used as a measure of cost and their ratio is taken to ensure that the efficient classifier is selected. The experimental results show that the performance of proposed relational classification is better in terms of efficiency when compared to all other existing algorithms available in the literature. We are able to achieve best results by selecting an efficient algorithm for every relation contributing in the relational classification.