YAILS differs from the AQ-family [10] programs in several aspects. AQ-type algorithms perform unidirectional search. In general they start with an empty complex and proceed by adding conditions. YAILS uses a bi-directional search. AQ-type programs use a covering search strategy. This means that they start with a set of uncovered examples and each time an example is covered by some rule the example is removed from the set. Their goal is to make this set empty. In YAILS this is not the case thus enabling the production of redundant rules.
The main differences stated between YAILS and AQ-type programs also hold in comparison to CN2 [4] with the addition that CN2 is non-incremental. In effect CN2 has a search strategy that is similar to AQ with the difference of using ID3-like information measures to find the attributes to use in specialisation.
STAGGER [14] system uses weights to characterise its concept descriptions. In STAGGER each condition has two weights attached to it. These weights are a kind of counters of correct and incorrect matching of the condition. In YAILS, the weights represent the decrease of entropy obtained by the addition of each condition. This means that the weights express the information content of the condition with respect to the conclusion of the rule. STAGGER also performs bi-directional search using three types of operators: specialisation, generalisation and inversion (negation). The main differences are that STAGGER learns only one concept (ex. rain / not rain) and uses only boolean attributes. YAILS differs from STAGGER in that it uses redundancy and flexible matching.
The work of Gams [6] on redundancy clearly showed the advantages of redundancy. In his work Gams used several knowledge bases which were used in parallel to obtain the classification of new instances. This type of redundancy demands a good conflict resolution strategy in order to take advantage of the diversity of opinions. The same point could be raised in YAILS with respect to the combination of different rules. In [13] we present an experimental analysis of several different combination strategies.
The work by Brazdil and Torgo [2] is also related to this. It consisted of combining several knowledge bases obtained by different algorithms into one knowledge base. Significant increase in performance was observed showing the benefits of multiple sources of knowledge.