EFFECT OF SAMPLING SIZE ON DATA MINING USING ARTIFICIAL NEURAL NETWORKS

Abstract

Artificial Neural Networks (ANNs) represent a useful technique for data mining applications. They can be trained to properly represent various categories occurring in a data set. In large databases, and data warehousing techniques, the size of data sets can be huge which may result in inefficient ANNs learning. Thus, it is useful to find an efficient and practical training set size without compromising the results. This paper presents experimental results highlighting the effect of varying the sampling size used in training Artificial Neural Networks and demonstrates that the extra effort used in expanding the training set is not linearly proportional to the improved accuracy. These results are important, and they are currently validated on a variety of domains