K-NN Ensemble DARA Approach to Learning Relational

Abstract

Due to the growing amount of data generated and stored in relational databases, relational learning has attracted the interest of researchers in recent years. Many approaches have been developed in order to learn relational data. One of the approaches used to learn relational data is Dynamic Aggregation of Relational Attributes (DARA). The DARA algorithm is designed to summarize relational data with one-to-many relations. However, DARA suffers a major drawback when the cardinalities of attributes are very high because the size of the vector space representation depends on the number of unique values that exist for all attributes in the dataset. A feature selection process can be introduced to overcome this problem. These selected features can then be further optimized to achieve a good classification result when using k Nearest Neighbour (k-NN) classifier. Several clustering runs can be performed for different values of k to yield an ensemble of clustering results. This paper proposes a two-layered genetic algorithm-based feature selection in order to improve the classification performance of learning relational database using a k-NN ensemble classifier. The proposed method involves the task of omitting less relevant features but retaining the diversity of the classifiers so as to improve the performance of the k-NN ensemble. Based on the results obtained, it shows that the proposed k-NN ensemble is able to improve the performance of traditional k-NN classifiers.