The effect of data character on empirical concept learning

The effect of data character on empirical concept learning has typically been studied using data from real domains. This presents problems because such data is often limited and uncontrollable. The authors present a more complex approach. They examine typical problems in an effort to characterize concepts more completely. They than utilize the characterization to generate data artificially. From this controlled data, they measure learning performance (speed and accuracy) as a precise function of several data characteristics. The authors´ experiments lead to some novel conclusions: a useful starting point to clarify data character is the definition of the term concept, which is effectively a function over instance space; characterizing a concept as a function allows the mimicking of natural data and the control of the generation of artificial data for extensive experimentation; data characteristics are numerous and easy to overlook; and, compared with significant design factors of learning algorithms, certain data characteristics are highly significant