Decision tables

Semantic Decision Tables: Self-organizing and Reorganizable Decision Tables

A Semantic Decision Table (SDT) provides a means to capture and examine decision makers’ concepts, as well as a tool for refining their decision knowledge and facilitating knowledge sharing in a scalable manner. One challenge SDT faces is to organize decision resources represented in a tabular format based on the user’s needs at different levels. It is important to make it self organized and au...

On Learnability of Decision Tables

The article is exploring the learnabilty issues of decision tables acquired from data within the frameworks of rough set and of variable precision rough set models. Measures of learning problem complexity and of learned table domain coverage are proposed. Several methods for enhancing the learnabilty of decision tables are discussed, including a new technique based on value reducts.

Rough sets and decision tables

The Power of Decision Tables

We evaluate the power of decision tables as a hypothesis space for supervised learning algorithms. Decision tables are one of the simplest hypothesis spaces possible, and usually they are easy to understand. Experimental results show that on artiicial and real-world domains containing only discrete features, IDTM, an algorithm inducing decision tables, can sometimes outperform state-of-the-art ...

On Constructing Semantic Decision Tables

Decision tables are a widely used knowledge management tool in the decision making process. Ambiguity and conceptual reasoning difficulties arise while designing large decision tables in a collaborative environment. We introduce the notion of Semantic Decision Table (SDT), which enhances a decision table with explicit decision semantics by annotating it properly with a domain ontology. In this ...