The Complexity of a Probabilistic Approach to Deal with Missing Values in a Decision Tree

Autor:	Lamis Hawarah, Ana Simonet, Michel Simonet
Rok vydání:	2006
Předmět:	Incremental decision tree business.industry Decision tree learning Decision tree Classification Tree Method Mutual information Missing data computer.software_genre Machine learning Alternating decision tree Data mining Artificial intelligence business computer Decision tree model Mathematics
Zdroj:	SYNASC
Popis:	We describe the complexity of an approach to fill missing values in decision trees during classification. This approach is derived from the ordered attribute trees method 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 into account the dependence between attributes by using Mutual Information. The result of the classification process is a probability distribution instead of a single class. In this paper, we explain our classification algorithm. We then calculate the complexity of constructing the attribute trees and the complexity of classifying a new instance with missing values using our classification algorithm.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::2c67f902b47115883bb8314009ce3c16 https://doi.org/10.1109/synasc.2006.70 Zobrazit plný text záznamu