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The Classification of Data: A Novel Artificial Neural Network (ANN) Approach through Exhaustive Validation and Weight Initialization

Kshitij Tripathi1 , Rajendra G. Vyas2 , Anil K. Gupta3

  1. Department of Computer Applications, The Maharaja Sayajirao University of Baroda,Vadodara, India.
  2. Department of Mathematics, The Maharaja Sayajirao University of Baroda, Vadodara, India.
  3. Department of Computer Science and Applications, Barkatullah University, Bhopal, India.

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-5 , Page no. 241-254, May-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i5.241254

Online published on May 31, 2018

Copyright © Kshitij Tripathi, Rajendra G. Vyas, Anil K. Gupta . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Kshitij Tripathi, Rajendra G. Vyas, Anil K. Gupta, “The Classification of Data: A Novel Artificial Neural Network (ANN) Approach through Exhaustive Validation and Weight Initialization,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.241-254, 2018.

MLA Style Citation: Kshitij Tripathi, Rajendra G. Vyas, Anil K. Gupta "The Classification of Data: A Novel Artificial Neural Network (ANN) Approach through Exhaustive Validation and Weight Initialization." International Journal of Computer Sciences and Engineering 6.5 (2018): 241-254.

APA Style Citation: Kshitij Tripathi, Rajendra G. Vyas, Anil K. Gupta, (2018). The Classification of Data: A Novel Artificial Neural Network (ANN) Approach through Exhaustive Validation and Weight Initialization. International Journal of Computer Sciences and Engineering, 6(5), 241-254.

BibTex Style Citation:
@article{Tripathi_2018,
author = {Kshitij Tripathi, Rajendra G. Vyas, Anil K. Gupta},
title = {The Classification of Data: A Novel Artificial Neural Network (ANN) Approach through Exhaustive Validation and Weight Initialization},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {5 2018},
volume = {6},
Issue = {5},
month = {5},
year = {2018},
issn = {2347-2693},
pages = {241-254},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=1969},
doi = {https://doi.org/10.26438/ijcse/v6i5.241254}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i5.241254}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=1969
TI - The Classification of Data: A Novel Artificial Neural Network (ANN) Approach through Exhaustive Validation and Weight Initialization
T2 - International Journal of Computer Sciences and Engineering
AU - Kshitij Tripathi, Rajendra G. Vyas, Anil K. Gupta
PY - 2018
DA - 2018/05/31
PB - IJCSE, Indore, INDIA
SP - 241-254
IS - 5
VL - 6
SN - 2347-2693
ER -

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Abstract

The Artificial Neural Networks (ANNs) has proved their significance to perform well in the fields of data-mining and machine learning like classification, pattern recognition, forecasting and prediction to have a few of them. This paper explores a novel approach for classification of data on four benchmark datasets from the perspective of ANNs and its intricacies. The proposed approach is successful in overcoming the drawback of over-fitting of data exists in the classification domain. Further, the proposed methodology reflects very improved and consistent results in comparison to existing techniques available in the ANNs as well as non-ANN domain.

Key-Words / Index Term

Classification, Artificial Neural Network, Machine Learning, N-Fold Cross Validation, Transfer function.

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