This paper presents the learning system YAILS capable of obtaining high accuracy in noisy domains. One of the novel features of the program is its controlled use of redundancy. Several authors ([5, 7, 2]) reported experiments that clearly show an increase in accuracy when multiple sources of knowledge are used. On the other hand, the existence of redundancy decreases the comprehensibility of learned theories. The controlled use of redundancy enables YAILS to better solve problems of uncertainty common in real world domains. Another important feature of the system is its mechanism of weighted flexible matching. This feature also contributes for the better handling of noisy domains. In terms of learning procedures the system uses a bi-directional search procedure opposed to the traditional bottom-up or top-down search common in other systems.
The next section gives a description of some of the main issues on the YAILS learning algorithm. The following section describes the classification strategies used by YAILS. Finally, section 4 describes several experiments carried out with YAILS that show the effect of redundancy on both accuracy and comprehensibility.