A Review of Evolutionary Algorithms for Data Mining <s> Multiple-Attributes-per-Individual Representation <s> For a given data set, its set of attributes defines its data space representation. The quality of a data space representation is one of the most important factors influencing the performance of a data mining algorithm. The attributes defining the data space can be inadequate, making it difficult to discover high-quality knowledge. In order to solve this problem, this paper proposes a Genetic Programming algorithm developed for attribute construction. This algorithm constructs new attributes out of the original attributes of the data set, performing an important preprocessing step for the subsequent application of a data mining algorithm. <s> BIB001 </s> A Review of Evolutionary Algorithms for Data Mining <s> Multiple-Attributes-per-Individual Representation <s> The use of machine learning techniques to automatically analyse data for information is becoming increasingly widespread. In this paper we examine the use of Genetic Programming and a Genetic Algorithm to pre-process data before it is classified using the C4.5 decision tree learning algorithm. The Genetic Programming is used to construct new features from those available in the data, a potentially significant process for data mining since it gives consideration to hidden relationships between features. The Genetic Algorithm is used to determine which such features are the most predictive. Using ten well-known datasets we show that our approach, in comparison to C4.5 alone, provides marked improvement in a number of cases. <s> BIB002 </s> A Review of Evolutionary Algorithms for Data Mining <s> Multiple-Attributes-per-Individual Representation <s> In this paper we use genetic programming for changing the representation of the input data for machine learners. In particular, the topic of interest here is feature construction in the learning-from-examples paradigm, where new features are built based on the original set of attributes. The paper first introduces the general framework for GP-based feature construction. Then, an extended approach is proposed where the useful components of representation (features) are preserved during an evolutionary run, as opposed to the standard approach where valuable features are often lost during search. Finally, we present and discuss the results of an extensive computational experiment carried out on several reference data sets. The outcomes show that classifiers induced using the representation enriched by the GP-constructed features provide better accuracy of classification on the test set. In particular, the extended approach proposed in the paper proved to be able to outperform the standard approach on some benchmark problems on a statistically significant level. <s> BIB003 </s> A Review of Evolutionary Algorithms for Data Mining <s> Multiple-Attributes-per-Individual Representation <s> In this paper, we present a general-purpose, systematic algorithm, consisting of a genetic programming module and a k-nearest neighbor classifier, to automatically create multiple artificial features (i.e., features that are computer-crafted and may not have a known physical meaning) directly from EEG signals, in a process that reveals patterns predictive of epileptic seizures. The algorithm was evaluated in three patients, with prediction defined over a horizon that varies between 1 and 5 minutes before unequivocal electrographic onset of seizure. For one patient, a perfect classification was achieved. For the other two patients, high classification accuracy was reached, predicting three seizures out of four for one, and eleven seizures out of fifteen for the other. For the latter, also, only one normal (non-seizure) signal was misclassified. These results compare favorably with other prediction approaches for patients from the same population. <s> BIB004
In several GPs for attribute construction, each individual represents a single constructed attribute. This approach is used for instance by CPGI  ) and the GP algorithm proposed by BIB001 . By default this approach returns to the user a single constructed attribute -the best evolved individual. However it can be extended to return to the user a set of constructed attributes, say returning a set of the best evolved individuals of a GP run or by running the GP multiple times and returning only the best evolved individual of each run. The main advantage of this approach is simplicity, but it has the disadvantage of ignoring interactions between the constructed attributes. An alternative approach consists of associating with an individual a set of constructed attributes. The main advantage of this approach is that it takes into account interaction between the constructed attributes. In other words, it tries to construct the best set of attributes, rather than the set of best attributes. The main disadvantages are that the individuals' genomes become more complex and that it introduces the need for additional parameters such as the number of constructed attributes that should be encoded in one individual (a parameter that is usually specified in an ad-hoc fashion). In any case, the equivalent of this latter parameter would also have to be specified in the above-mentioned "extended version" of the single-attribute-per-individual approach when one wants the GP algorithm to return multiple constructed attributes. Examples of this multiple-attributes-per-individual approach are the GP algorithms proposed by BIB003 BIB002 BIB004 ). Here we briefly discuss the former two, as examples of this approach. In (Krawiec 2002) each individual encodes a fixed number K of constructed attributes, each of them represented by a tree, so that an individual consists of K treeswhere K is a user-specified parameter. The algorithm also includes a method to split the constructed attributes encoded in an individual into two subsets, namely the subset of "evolving" attributes and the subset of "hidden" attributes. The basic idea is that high-quality constructed attributes are considered hidden (or "protected"), so that they cannot be manipulated by the genetic operators such as crossover and mutation. The choice of attributes to be hidden is based on an attribute quality measure. This measure evaluates the quality of each constructed attribute separately, and the best attributes of the individual are considered hidden. Another example of the multiple-attributes-per-individual approach is the GAP (Genetic Algorithm and Programming) system proposed by BIB002 . GAP performs both attribute construction and attribute selection. The first stage consists of attribute construction, which is performed by a GP algorithm. As a result of this first stage, the system constructs an extended genotype containing both the constructed attributes represented in the best evolved individual of the GP run and original attributes that have not been used in those constructed attributes. This extended genotype is used as the basic representation for a GA that performs attribute selection, so that the GA searches for the best subset of attributes out of all (both constructed and original) attributes.