Missing Values in Data Mining - Subhendu Pani
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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 |