Ensemble rule based classification methods have been popular for a while in the machine-learning literature (Hand, 1997). Given the advent of low-cost, high-computing power, we are curious to see how far can we go by repeating some basic learning process, obtaining a variety of possible inferences, and finally basing the global classification decision on some sort of ensemble summary. Some general benefits to this idea have been observed indeed, and we are gaining wider and deeper insights on exactly why this is the case in many fronts of interest. There are many ways to approach the ensemblebuilding task. Instead of locating ensemble members independently, as in Bagging (Breiman, 1996), or with little feedback from the joint behavior of the forming ensemble, as in Boosting (see, e.g., Schapire & Singer, 1998), members can be created at random and then made subject to an evolutionary process guided by some fitness measure. Evolutionary algorithms mimic the process of natural evolution and thus involve populations of individuals (rather than a single solution iteratively improved by hill climbing or otherwise). Hence, they are naturally linked to ensemble-learning methods. Based on the long-term processing of the data and the application of suitable evolutionary operators, fitness landscapes can be designed in intuitive ways to prime the ensemble’s desired properties. Most notably, beyond the intrinsic fitness measures typically used in pure optimization processes, fitness can also be endogenous, that is, it can prime the context of each individual as well.
A number of evolutionary mining algorithms are available nowadays. These algorithms may differ in the nature of the evolutionary process or in the basic models considered for the data or in other ways. For example, approaches based on the genetic programming (GP), evolutionary programming (EP), and classifier system (CS) paradigms have been considered, while predictive rules, trees, graphs, and other structures been have evolved. See Eiben and Smith (2003) for an introduction to the general GP, EP, and CS frameworks and Koza, Keane, Streeter, Mydlowec, Yu, and Lanza (2003) for an idea of performance by GP algorithms at the patent level. Here I focus on ensemble –rule based methods for classification tasks or supervised learning (Hand, 1997).
The CS architecture is naturally suitable for this sort of rule assembly problems, for its basic representation unit is the rule, or classifier (Holland, Holyoak, Nisbett, & Thagard, 1986). Interestingly, tentative ensembles in CS algorithms are constantly tested for successful cooperation (leading to correct predictions). The fitness measure seeks to reinforce those classifiers leading to success in each case. However interesting, the CS approach in no way exhausts the scope of evolutionary computation ideas for ensemble-based learning; see, for example, Kuncheva and Jain (2000), Liu, Yao, and Higuchi (2000), and Folino, Pizzuti, and Spezzano (2003).
Ensembles of trees or rules are the natural reference for evolutionary mining approaches. Smaller trees, made by rules (leaves) with just a few tests, are of particular interest. Stumps place a single test and constitute an extreme case (which is nevertheless used often). These rules are more general and hence tend to make more mistakes, yet they are also easier to grasp and explain. A related notion is that of support, the estimated probability that new data satisfy a given rule’s conditions. A great deal of effort has been done in the contemporary CS literature to discern the idea of adequate generality, a recurrent topic in the machine-learning arena.