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- W2144258382 abstract "Data mining is the study of how to determine underlying pat- terns in the data to help make optimal decisions on computers when the database involved is voluminous, hard to characterize accurately, and con- stantly changing. It deploys techniques based on machine learning, along- side the conventional methods. More importantly, these techniques can gen- erate decision or prediction models, based on the actual historical data. Therefore, they represent true evidence-based decision support. Rainfall prediction is a good problem to solve by these data mining techniques. This paper proposes an improved Naive Bayes classifier (INCB) technique and explores the use of genetic algorithms (GAs) for selection of a subset of input features in classification problems. It then carries out a comparison with several other techniques. It sets a comparison of the following algorithms, namely: 1) genetic algorithm with average classification or general classi- fication (GA-AC, GA-C) ,2 )C4.5 with pruning, and 3) INBC with relative frequency or initial probability density (INBC-RF, INBC-IPD) on the real meteorological data in Hong Kong. Two simple schemes are proposed to construct a suitable data set for improving the performance. Scheme I uses all basic input parameters for rainfall prediction. Scheme II uses the op- timal subset of input variables which are selected by a GA. The results show that among the methods we compared, INBC achieved about 90% accuracy rate on the rain/no-rain (Rain) classification problems. This method also at- tained reasonable performance on rainfall prediction with three-level depth (Depth3) and five-level depth (Depth5), which are around 65%-70%. Index Terms—Data mining, genetic algorithm (GA), improved Naive Bayes classification (INCB), rainfall prediction." @default.
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- W2144258382 date "2001-01-01" @default.
- W2144258382 modified "2023-09-27" @default.
- W2144258382 title "An Improved Naïve Bayesian Classifier Technique Coupled With a Novel Input Solution Method" @default.
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