A Survey of Evolutionary Algorithms for Data Mining and Knowledge Discovery <s> Representing the Rule Antecedent (a Conjunction of Conditions). <s> Staple fibers made from crimped, in particular compression crimped, multicomponent filaments of the matrix/segment type, the cross section of which shows, in addition to the matrix, at least 6 peripheral wedge-shaped or lenticular segments, not completely covered by the matrix, are processed into a web, which is then mechanically bonded, preferably by needling. Subsequently, the fabric is subjected to a shrinkage process, by which its density is increased by more than 30%, themulticomponent fibers being completely or partly split up into their components. The difference in shrinkage between the components should be at least 10%. The liquids used for the shrinkage treatment are in particular organic liquids, such as methylene chloride, as well as other liquids producing a difference in shrinkage of at least 20%. The fabric is then impregnated with a solution of polyurethane on the basis of polyglycols, specifically polytetramethylene glycol, diisocyanates and low-molecular glycols as chain lengtheners, the jelling temperature of the solution being higher than the roomtemperature and higher than the temperature of the coagulation bath; the polyurethane is coagulated by cooling and/or treatment with a coagulation bath containing a non-solvent for polyurethane; then the fabric is washed, dried and ground on one or both sides. The suede-like product obtained combines great suppleness with high strength. It is an excellent material for making garments, such as coats, jackets or skirts, offering high wear comfort as well as high breathability. <s> BIB001 </s> A Survey of Evolutionary Algorithms for Data Mining and Knowledge Discovery <s> Representing the Rule Antecedent (a Conjunction of Conditions). <s> Decision rules are a natural form of representing knowledge. Their extraction from databases requires the capability for effective search large solution spaces. This paper shows, how we can deal with this problem using evolutionary algorithms (EAs). We propose an EA-based system called EDRL, which for each class label sequentially generates a disjunctive set of decision rules in propositional form. EDRL uses an EA to search for one rule at a time; then, all the positive examples covered by the rule are removed from the learning set and the search is repeated on the remaining examples. Our version of EA differs from standard genetic algorithm. In addition to the well-known uniform crossver it employs two non-standard genetic operators, which we call changing condition and insertion. Currently EDRL requires prior discretization of all continuous-valued attributes. A discretization technique based on the minimization of class entropy is used. The performance of EDRL is evaluated by comparing its classification accuracy with that of C4.5 learning algorithm on six datasets from UCI repository. <s> BIB002 </s> A Survey of Evolutionary Algorithms for Data Mining and Knowledge Discovery <s> Representing the Rule Antecedent (a Conjunction of Conditions). <s> In this article, we explore the use of genetic algorithms (GAs) as a key element in the design and implementation of robust concept learning systems. We describe and evaluate a GA-based system called GABIL that continually learns and refines concept classification rules from its interaction with the environment. The use of GAs is motivated by recent studies showing the effects of various forms of bias built into different concept learning systems, resulting in systems that perform well on certain concept classes (generally, those well matched to the biases) and poorly on others. By incorporating a GA as the underlying adaptive search mechanism, we are able to construct a concept learning system that has a simple, unified architecture with several important features. First, the system is surprisingly robust even with minimal bias. Second, the system can be easily extended to incorporate traditional forms of bias found in other concept learning systems. Finally, the architecture of the system encourages explicit representation of such biases and, as a result, provides for an important additional feature: the ability to dynamically adjust system bias. The viability of this approach is illustrated by comparing the performance of GABIL with that of four other more traditional concept learners (AQ14, C4.5, ID5R, and IACL) on a variety of target concepts. We conclude with some observations about the merits of this approach and about possible extensions. <s> BIB003
A simple approach to encode rule conditions into an individual is to use a binary encoding. Suppose that a given attribute can take on k discrete values. Then we can encode a condition on the value of this attribute by using k bits. The i-th value (i=1,...,k) of the attribute domain is part of the rule condition if and only if the i-th bit is "on" BIB003 . For instance, suppose that a given individual represents a rule antecedent with a single attribute-value condition, where the attribute is Marital_Status and its values can be "single", "married", "divorced" and "widow". Then a condition involving this attribute would be encoded in the genome by four bits. If these bits take on, say, the values "0 1 1 0" then they would be representing the following rule antecedent: Hence, this encoding scheme allows the representation of conditions with internal disjunctions, i.e. with the logical OR operator within a condition. Obviously, this encoding scheme can be easily extended to represent rule antecedents with several conditions (linked by a logical AND) by including in the genome an appropriate number of bits to represent each attribute-value condition. Note that if all the k bits of a given rule condition are "on", this means that the corresponding attribute is effectively being ignored by the rule antecedent, since any value of the attribute satisfies the corresponding rule condition. In practice, it is desirable to favor rules where some conditions are "turned off" -i.e. have all their bits set to "1" -in order to reduce the size of the rule antecedent. (Recall that we want comprehensible rules and, in general, the shorter the rule is the more comprehensible it is.) To achieve this, one can automatically set all bits of a condition to "1" whenever more than half of those bits are currently set to "1". Another technique to achieve the same effect will be discussed at the end of this subsection. The above discussion assumed that the attributes were categorical, also called nominal or discrete. In the case of continuous attributes the binary encoding mechanism gets slightly more complex. A common approach is to use bits to represent the value of a continuous attribute in binary notation. For instance, the binary string "0 0 0 0 1 1 0 1" represents the value 13 of a given integer-valued attribute. Instead of using a binary representation for the genome of an individual, this genome can be expressed in a higher-level representation which directly encodes the rule conditions. One of the advantages of this representation is that it leads to a more uniform treatment of categorical and continuous attributes, in comparison with the binary representation. In any case, in rule discovery we usually need to use variable-length individuals, since, in principle, we do not know a priori how many conditions will be necessary to produce a good rule. Therefore, we might have to modify crossover to be able to cope with variable-length individuals in such a way that only valid individuals are produced by this operator. For instance, suppose that we use a high-level representation for two individuals to be mated, as follows (there is an implicit logical AND connecting the rule conditions within each individual): (Age > 25) (Marital_Status = "Married") (Has_a_job = "yes") (Age < 21) As a result of a crossover operation, one of the children might be an invalid individual (i.e. a rule with contradicting conditions), such as the following rule antecedent: IF (Age > 25) AND (Age < 21). To avoid this, we can modify the individual representation to encode attributes in the same order that they occur in the data set, including in the representation "empty conditions" as necessary. Continuing the above example, and assuming that the data set being mined has only the attributes Age, Marital_Status, and Has_a_job, in this order, the two above individuals would be encoded as follows: (Age > 25) (Marital_Status = "married") ("empty conditon") (Age < 21) ("empty condition") (Has_a_job = "yes") Now each attribute occupies the same position in the two individuals, i.e. attributes are aligned BIB001 . Hence, crossover will produce only valid individuals. This example raises the question of how to determine, for each gene, whether it represents a normally-expressed condition or an empty condition. A simple technique for solving this problem is as follows. Suppose the data being mined contains m attributes. Then each individual contains m genes, each of them divided into two parts. The first one specifies the rule condition itself (e.g. Age > 25), whereas the second one is a single bit. If this bit is "on" ("off") the condition is included in (excluded from) the rule antecedent represented by the individual. In other words, the "empty conditions" in the above example are represented by turning off this bit. Since we want rule antecedents with a variable number of conditions, this bit is usually subject to the action of genetic operators BIB002 .