APACS: a system for the automatic analysis and classification of conceptual patterns

Many existing inductive learning systems have been developed under the assumption that the learning tasks are performed in a noise‐free environment. To cope with most real‐world problems, it is important that a learning system be equipped with the capability to handle uncertainty. In this paper, we first identify the various sources of uncertainty that may be encountered in a noisy problem domain. Next, we present a method for the efficient acquisition of classification rules from training instances which may contain inconsistent, incorrect, or missing information. This algorithm consists of three phases: (i) the detection of inherent patterns in a set of noisy training data; (ii) the construction of classification rules based on these patterns; and (iii) the use of these rules to predict the class membership of an object. The method has been implemented in a system known as APACS (automatic pattern analysis and classification system). This system has been tested using both real‐life and simulated data, and its performance is found to be superior to many existing systems in terms of efficiency and classification accuracy. Being able to handle uncertainty in the learning process, the proposed algorithm can be employed for applications in real‐world problem domains involving noisy data.