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| Titel: |
A GENERATOR BASED ASSOCIATIVE CLASSIFIER FOR IMBALANCED DATASETS |
| Auteur: |
SIREESHA RODDA, PROF. SHASHI MOGALLA |
| Verschenen in: |
International journal of engineering science and technology |
| Paginering: | Jaargang 3 (2011) nr. 4 pagina's 2685-2692 |
| Jaar: |
2011 |
| Inhoud: |
In this paper, a new variant of Associative Classifier has been proposed. Most Associative Classifiers perform classification on Class Association Rules obtained from a complete set of Frequent Itemsets. The main disadvantage of associative classifiers is that huge number of class association rules are generated. Handling large set of class association rules is a challenging problem. In this paper, a new framework has been proposed which uses class association rules generated from a complete set of frequent generators. The classification rules thus obtained are smaller in size, lesser in total number and non-redundant in nature when compared to those obtained from classification rules generated by a complete set of frequent itemsets. Furthermore, the scalability of the classifier is ensured by the use of trie datastructure to store the set of frequent generators which directly represent the class association rules in this case. Experiments and comparison against classifiers like C4.5and variants on imbalanced and balanced data confirm our claims. |
| Uitgever: |
Engg Journals Publication (provided by DOAJ) |
| Bronbestand: |
Elektronische Wetenschappelijke Tijdschriften |
Details van artikel 6 van 122 gevonden artikelen
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