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Techniques Tanimoto correlated feature selection system and hybridization of clustering and boosting ensemble classification of remote sensed big data for weather forecasting
تکنیک های مربوط به سیستم انتخاب ویژگی Tanimoto و ترکیبی از خوشه بندی و افزایش طبقه بندی گروه از داده های بزرگ از راه دور برای پیش بینی آب و هوا-2020 Weather forecasting has been done using various techniques but still not efficient for handling the big remote
sensed data since the data comprises the more features. Hence the techniques degrade the forecasting accuracy
and take more prediction time. To enhance the prediction accuracy (PA) with minimal time, Tanimoto
Correlation based Combinatorial MAP Expected Clustering and Linear Program Boosting Classification (TCCMECLPBC)
Technique is proposed. At first, the data and features are gathered from big weather database.
After that, relevant features are selected through finding the similarity between the features. Tanimoto
Correlation Coefficient is used to find the similarity between the features for selecting the relevant features
with higher feature selection accuracy. After selecting the relevant features, MAP expected clustering process
is carried out to group the weather data for cluster formation. In this process, a number of cluster and
cluster centroids are initialized. In this clustering process, it includes two steps namely expectation (E)
and maximization (M) to discover maximum probability for grouping data into the cluster. After that, the
clustering result is given to Linear Program boosting classifier to improve the prediction performance. In this
classification, the weak classifier results are boosted to create strong classifier. The results evident that the
TC-CMECLPBC technique enhance the PA with lesser time and false positive rate (FPR) than the conventional
methods. Keywords: Big data | Tanimoto correlation | MAP expected | Boosting classification | Expectation | Maximization | Similarity | Clustering | Cluster centroids | Strong classifier | Weak classifier |
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