IJBTT

Kernel zed Target Feature Projection-based Implicit Indexive Bootstrap Aggregating Classifier for Marine Weather Forecasting with Big Data

Kernel zed Target Feature Projection-based Implicit Indexive Bootstrap Aggregating Classifier for Marine Weather Forecasting with Big Data
	© 2022 by IJETT Journal
	Volume-70 Issue-8
	Year of Publication : 2022
	Authors : Deepa Anbarasi J, V. Radha
	DOI : 10.14445/22315381/IJETT-V70I8P204

How to Cite?

Deepa Anbarasi J, V. Radha, "Kernel zed Target Feature Projection-based Implicit Indexive Bootstrap Aggregating Classifier for Marine Weather Forecasting with Big Data," International Journal of Engineering Trends and Technology, vol. 70, no. 8, pp. 42-50, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I8P204

Abstract
Weather forecasting is a computer program that offers meteorological information to forecast the atmospheric conditions for a particular location. It has been done by using enormous techniques but is still not enough for handling big data since the data consists of a more volume of data. Therefore, the techniques do not show the forecasting accuracy perfectly and take more prediction time. To improve the prediction accuracy with lesser time, A Fisher Kernelized Target Feature Projection-based Implicit Morisita-Horn Indexive Decision Stumped Bootstrap Aggregating Classification (FKTFPIMHIDSBAC) technique is introduced for forecasting higher accuracy and less time consumption of marine weather. The proposed IUMHIDSBAC technique consists of two main processes: feature selection and classification, which are carried out using Fisher Kernelized Target Feature Projection. The feature selection process of the proposed FKTFP-IMHIDSBAC technique has reduced the time complexity of the prediction. Then Implicit Morisita-Horn Indexive Decision Stumped Bootstrap Aggregating Classifier is applied for weather forecasting with the selected features. The Bootstrap Aggregating Classifier is an ensemble technique that uses the weak learners as a Morisita-Horn Indexive Decision Stump for analyzing the testing and training data. Then the ensemble classifier combines the weak learner and applies the implicit utilitarian voting scheme to find accurate results and minimize the error. The results and discussion demonstrate that the proposed FKTFPIMHIDSBAC technique increases the accuracy and minimizes the error as well as target tracking time than the existing techniques.

Keywords
Marine weather forecasting, Big data, Fisher Kernelized Target Feature Projection, Implicit Morisita-Horn Indexive Decision Stumped Bootstrap Aggregating Classifier.

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