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- W3128194091 abstract "AbstractData mining is defined as a knowledge discovery process, which uses several algorithms for various applications. Classification of data objects based on an efficient classification algorithm is a very important task to be carried out on a huge dataset. It can also be defined as a supervised learning algorithm in which classification is performed on a predefined knowledge of the objects and assigns class labels based on the relationship. Decision trees are the mainly used classification algorithm. In decision trees, entropy is calculated and maximum likelihood has been used which assumes probability distributions for the input labels. The influence of prior probabilities in non-parametric classification methods has proven to be useful only for particular classes of interest. To accomplish the objective such as better classification accuracy and to reduce the elimination of sparse data from classification, a non-parametric kernel function with modified entropy is implemented in the classification algorithm. This proposed methodology based on estimation proves that it provides good classification outcome for the frequency distribution of dataset irrespective of the decision boundaries and complexity of the datasets. The relative comparisons of the results and the consistency were examined with different datasets, and the results performed well in all the experiments conducted.KeywordsNon-parametricKernel density estimationClassification treeEntropy" @default.
- W3128194091 created "2021-02-15" @default.
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- W3128194091 date "2021-01-01" @default.
- W3128194091 modified "2023-10-18" @default.
- W3128194091 title "Optimizing Decision Tree Classification Algorithm with Kernel Density Estimation" @default.
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- W3128194091 doi "https://doi.org/10.1007/978-981-15-9651-3_22" @default.
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