Evaluation of a Probabilistic Approach to Classify Incomplete Objects Using Decision Trees

Autor:	Lamis Hawarah, Michel Simonet, Ana Simonet
Rok vydání:	2006
Předmět:	business.industry Computer science Decision tree Probabilistic logic Mutual information Machine learning computer.software_genre Missing data Class (biology) Expert system Probability distribution Data mining Artificial intelligence business computer Test data
Zdroj:	Lecture Notes in Computer Science ISBN: 9783540378716 DEXA
Popis:	We describe an approach to fill missing values in decision trees during classification. This approach is derived from the ordered attribute trees method, proposed by Lobo and Numao in 2000, which builds a decision tree for each attribute and uses these trees to fill the missing attribute values. Both our approach and theirs are based on the Mutual Information between the attributes and the class. Our method takes the dependence between attributes into account by using the Mutual Information. The result of the classification process is a probability distribution instead of a single class. In this paper, we present tests performed on some real databases using our approach and Quinlan's method. We analyse the classification results of some instances in test data and finally we discuss some perspectives.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::bbc9e1798f6c0dee6662971b50f6d3fb https://doi.org/10.1007/11827405_19 Zobrazit plný text záznamu