Deep Neural Networks for the Classification of Bank Marketing Data using Data Reduction Techniques
Chittem Leela Krishna1, Poli Venkata Subba Reddy2

1Chittem Leela Krishna*, Research Scholar, Dept. of CSE, S.V.U. College of Engineering, S.V. University, Tirupati, India.
vsrpoli@hotmail.comn]2Dr. Poli Venkata Subba Reddy, Professor, Dept. of CSE, S.V.U. College of Engineering, S.V. University, Tirupati, India. 

Manuscript received on 11 August 2019. | Revised Manuscript received on 28 August 2019. | Manuscript published on 30 September 2019. | PP: 4373-4378 | Volume-8 Issue-3 September 2019 | Retrieval Number: C5522098319/2019©BEIESP | DOI: 10.35940/ijrte.C5522.098319
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Abstract: The amount of data belonging to different domains are being stored rapidly in various repositories across the globe. Extracting useful information from the huge volumes of data is always difficult due to the dynamic nature of data being stored. Data Mining is a knowledge discovery process used to extract the hidden information from the data stored in various repositories, termed as warehouses in the form of patterns. One of the popular tasks of data mining is Classification, which deals with the process of distinguishing every instance of a data set into one of the predefined class labels. Banking system is one of the real-world domains, which collects huge number of client data on a daily basis. In this work, we have collected two variants of the bank marketing data set pertaining to a Portuguese financial institution consisting of 41188 and 45211 instances and performed classification on them using two data reduction techniques. Attribute subset selection has been performed on the first data set and the training data with the selected features are used in classification. Principal Component Analysis has been performed on the second data set and the training data with the extracted features are used in classification. A deep neural network classification algorithm based on Backpropagation has been developed to perform classification on both the data sets. Finally, comparisons are made on the performance of each deep neural network classifier with the four standard classifiers, namely Decision trees, Naïve Bayes, Support vector machines, and k-nearest neighbors. It has been found that the deep neural network classifier outperforms the existing classifiers in terms of accuracy.
Keywords– Data Mining, Classification, Attribute Subset Selection, Principal Component Analysis, Deep Neural Networks

Scope of the Article: Classification