UGC Approved Journal no 63975(19)

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Published in:

Volume 11 Issue 1
January-2024
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIRTHE2081


Registration ID:
508881

Page Number

e257-e314

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Title

OPTIMIZATION OF A HYBRID-BASED RANDOM FOREST ALGORITHM FOR NETWORK SYSTEMS USING RANDOMIZED SEARCH HYPERPARAMETER TUNING METHOD

Abstract

Random Forest models have been providing a notable performance on her predictive capacity to applications in the realm of behavioural-based Intrusion Detection Systems and other related fields of specialization which includes medicines, Banking, commerce, etc in terms high magnitude forecasting and optimal predictions . In this work, in-depth evaluation analysis of the Random Forest tuning are carried out with respect to classification, feature selection, and proximity metrics. This empirical research will provide an inclusive review of the general basic concepts related to Intrusion Detection Systems, which includes taxonomies, data collection, modeling and evaluation metrics. This work further remodels the Random Forest algorithm using RandomizedSearchCV method hyperparameter tuning as base-behavioral classifier to check and compare with its default in terms of efficiency in the realm of machine learning. NSL-KDD dataset were used for both training and testing of the tuned model using a supervised learning method. The predictive performance in the tuned model with respect to its matrix was higher, and comparison with other algorithms like Naïve bayes and Perception model, Ridge classifier proved that the RandomizedSearchCV hyperparameter tuning Random Forest algorithm performed more efficiently its results analysis and computation.

Key Words

: RandomizedSearchCV, Hyperparameter, Decision Tree, Classifier, Random forest, Optimization, Tuning.

Cite This Article

"OPTIMIZATION OF A HYBRID-BASED RANDOM FOREST ALGORITHM FOR NETWORK SYSTEMS USING RANDOMIZED SEARCH HYPERPARAMETER TUNING METHOD", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 1, page no.e257-e314, January-2024, Available :http://www.jetir.org/papers/JETIRTHE2081.pdf

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2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"OPTIMIZATION OF A HYBRID-BASED RANDOM FOREST ALGORITHM FOR NETWORK SYSTEMS USING RANDOMIZED SEARCH HYPERPARAMETER TUNING METHOD", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 1, page no. ppe257-e314, January-2024, Available at : http://www.jetir.org/papers/JETIRTHE2081.pdf

Publication Details

Published Paper ID: JETIRTHE2081
Registration ID: 508881
Published In: Volume 11 | Issue 1 | Year January-2024
DOI (Digital Object Identifier):
Page No: e257-e314
Country: ENUGU, ENUGU, Nigeria .
Area: Science & Technology
ISSN Number: 2349-5162
Publisher: IJ Publication


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