23,99 €
inkl. MwSt.

Versandfertig in 6-10 Tagen
  • Broschiertes Buch

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.…mehr

Produktbeschreibung
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.
Autorenporträt
Ha più di 13 anni di esperienza nell'insegnamento dell'informatica e della tecnologia dell'informazione a livello UG e PG - Ha un'esperienza di ricerca di circa 10 anni, compreso il dottorato di ricerca. - Ha pubblicato oltre 100 articoli di ricerca in atti di conferenze e riviste di riferimento e ha pubblicato 5 libri nell'area del Data Mining e del Machine Learning.