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Missing Values in Data Mining - Subhendu Pani

Anglų
2017-03-30
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The effect of missing values on data classification is studied. A comparative analysis of data classification accuracy in different scenarios is presented. Several search techniques are considered in the study for feature selection and are applied to pre-process the dataset. The predictive performances of popular classifiers are compared quantitatively. The dataset is drawn from a breast cancer detection-de ... Visas aprašymas

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Aprašymas

The effect of missing values on data classification is studied. A comparative analysis of data classification accuracy in different scenarios is presented. Several search techniques are considered in the study for feature selection and are applied to pre-process the dataset. The predictive performances of popular classifiers are compared quantitatively. The dataset is drawn from a breast cancer detection-decision context available at UCI machine learning repository. After analysing the experimental results,the work establishes the general concept of improved classification accuracy using missing values replacement.

Daugiau informacijos

Autorius Subhendu Pani
Leidėjas LAP LAMBERT Academic Publishing
Išleidimo metai 2017
Viršelio tipas Minkšti viršeliai
EAN 9783659753138
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38,76 € 51,68 €