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- W3153852854 abstract "Accuracy of data classification strongly depends on the dataset used for learning. High dimensional datasets often suffer from the ‘curse of dimensionality’ problem which degrades the accuracy and performance of the classification models. Feature selection is an important step in building an effective and efficient classifier. It is used to choose an optimal subset of features according to an objective function. It leads to better performance of the classification models in terms of their predictive or descriptive accuracy. It also offers reduced computing time needed to build models as well as better understanding of the models. In this paper, performances of popular classifiers are compared quantitatively when data classification is performed based on feature selection. However, experimental results could not establish the general concept of improved classification accuracy using feature selection. Rather, the paper finds a mixed result for the classifiers considered in the study." @default.
- W3153852854 created "2021-04-26" @default.
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- W3153852854 date "2013-10-24" @default.
- W3153852854 modified "2023-09-23" @default.
- W3153852854 title "Performance Analysis of Data Classification Using Feature Selection" @default.
- W3153852854 hasPublicationYear "2013" @default.
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