Abstract
When discretization is used for preprocessing datasets in a decision system different representations of data can be taken into consideration. Typical approach is to use data as it is returned by discretizer, namely as nominal values. But in specific cases such form of data cannot be utilized by next modules of the decision system. Then the possible solution is to convert nominal data again into a numerical form. The paper presents comparison of such approaches applied for different classifiers in stylometry domain.
| Original language | English |
|---|---|
| Pages (from-to) | 1418-1427 |
| Number of pages | 10 |
| Journal | Procedia Computer Science |
| Volume | 96 |
| DOIs | |
| Publication status | Published - 2016 |
| Event | 20th International Conference on Knowledge Based and Intelligent Information and Engineering Systems, KES 2016 - York, United Kingdom Duration: 5 Sept 2016 → 7 Sept 2016 |
Keywords
- Bayesian network
- C4.5
- Naive Bayes
- decision tree
- discretization
- k-Nearest Neighbors
- nominal representation
- numerical representation
ASJC Scopus subject areas
- General Computer Science
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