Analysis of Speed, Accuracy of Deep Learning using Gini index, HSM based fuzzy decision trees

Authors

  • SVG Reddy Dept. of CSE, GIT (GITAM University), Visakhapatna, India
  • K Thammi Reddy Dept. of CSE, GIT (GITAM University), Visakhapatna, India
  • V Valli Kumari Dept. of CS and SE, College of Engineering (Andhra University), Visakhapatnam, India

DOI:

https://doi.org/10.26438/ijcse/v5i11.1624

Keywords:

Deep Learning, Heterogeneous split measure, gini index, weight, fuzzy, Decision trees, Classification Accuracy

Abstract

Deep Learning has gained tremendous importance due to its advancement in various fields of text mining, speech recognition, computer vision, natural language processing etc. The weights of the input layer attributes and the series of hidden layers of deep learning plays a dominant role in its classification speed and accuracy. The weight adjustment algorithm for the Deep Learning is proposed in this paper. Generally, the weights can be determined by mathematical techniques, can be suggested by the domain experts or by considering random weights. In this proposed work, the weights of a neural network are computed mathematically by constructing the fuzzy decision tree. It is proposed to use the maximum heterogeneous split measure(HSM) value of the attribute of the fuzzy decision tree as the weight of the corresponding attribute for the weight adjustment algorithm to classify using neural networks. Fast classification and accuracy is achieved with the computed HSM weights of the deep learning which outperforms when compared with the fuzzy decision tree classifiers. The same work was carried out using the least value of gini index. And in this paper the classification speed, accuracy is compared by considering the gini index and HSM based fuzzy decision trees and analyzed the results.

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Published

2025-11-12
CITATION
DOI: 10.26438/ijcse/v5i11.1624
Published: 2025-11-12

How to Cite

[1]
S. Reddy, K. Thammi Reddy, and V. Valli Kumari, “Analysis of Speed, Accuracy of Deep Learning using Gini index, HSM based fuzzy decision trees”, Int. J. Comp. Sci. Eng., vol. 5, no. 11, pp. 16–24, Nov. 2025.

Issue

Vol. 5 No. 11 (2017): IJCSE November Edition

Section

Research Article

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