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@article{158277,
        author = {Deepa Anbarasi J and Dr. V. Radha},
        title = {Fisher Kernelized Target Projective Feature Selection based Marine Weather Prediction with Big Data},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {9},
        pages = {186-194},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=158277},
        abstract = {Weather prediction is a computer program with meteorological information to forecast atmospheric conditions for specific location. Weather prediction is carried out using different techniques for managing the big data. Consequently, the existing technique reduced the feature selection accuracy with large time consumption. To enhance accuracy with lesser time, Fisher Kernelized Target Projective Feature Selection (FKTPFS) technique is introduced for weather prediction using higher accuracy as well as lesser time. In proposed FKTPFS technique, feature selection was carried out with Fisher Kernelized Target Projective Feature Selection for identifying significant features. The objective of Fisher Kernelized Target Projective Feature Selection is to give the large separation of class means while maintaining the in-class variance small. The feature selection process of proposed FKTPFS technique minimizes time complexity of the marine weather prediction. The results and discussion shows FKTPFS increases feature selection accuracy as well as minimize the error as well as feature selection time than the existing techniques.},
        keywords = {Marine Weather Prediction, Bigdata, Fisher Kernelized Target Projective Feature Selection, Meteorological Information},
        month = {},
        }