This paper introduces ICET, a new algorithm for cost-sensitive classification. ICET uses a genetic algorithm to evolve a population of biases for a decision tree induction algorithm. The fitness

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This paper describes REGAL, a distributed genetic algorithm-based system, designed for learning First Order Logic concept descriptions from examples. The system is a hybrid between the Pittsburgh and the Michigan approaches, as the population constitutes a redundant set of partial concept descriptions, each evolved separately. In order to increase effectiveness, REGAL is specifically tailored to the concept learning task; hence, REGAL is task-dependent, but, on the other hand, domain-independent. The system proved to be particularly robust with respect to parameter setting across a variety of different application domains. REGAL is based on a selection operator, called Universal Suffrage operator, provably allowing the population to asymptotically converge, in average, to an equilibrium state, in which several species coexist. The system is presented both in a serial and in a parallel version, and a new distributed computational model is proposed and discussed. The system has been test...

The completeness and consistency conditions were introduced in order to achieve acceptable concept recognition rules. In real problems, we can handle noise-affected examples and it is not always possible to maintain both conditions. Moreover, when we use fuzzy information there is a partial matching between examples and rules, therefore the consistency condition becomes a matter of degree. In this paper, a learning algorithm based on soft consistency and completeness conditions is proposed. This learning algorithm is tested on different databases. Keywords: machine learning, fuzzy sets, fuzzy rules, genetic algorithms This work has been supported by the CICYT under Project TIC92-0665 1 Introduction Inductive learning has been successfully applied to concept classification problems. Usually, the knowledge is represented through rules representing the relationships between the different problem variables. In this paper, we are interested in studying conditions that allow us to propos...

SLAVE (Structural Learning Algorithm in Vague Environment) is an inductive learning algorithm that uses concepts based on fuzzy logic theory. This theory has shown be an useful representation tool for improving the understanding under a human point of view, of the knowledge obtained. Furthermore, SLAVE uses an iterative approach for learning with genetic algorithms. This method is an alternative approach from the classical Pittsburgh and Michigan approaches. In this work, we propose some modifications of the original SLAVE learning algorithm, including new genetic operators for reducing the time needed for learning and improving the understanding of the rules obtained. Furthermore, we propose a new way for penalizing the rules in the iterative approach that permits to improve the behaviour of the system. Keywords: machine learning, fuzzy logic, genetic algorithms. 1 Introduction Inductive learning tries to extract a knowledge base that permits to describe the behaviour of a system fro...

Abstract. A new class of evolutionary computation processes is presented, called Learnable Evolution Model or LEM. In contrast to Darwinian-type evolution that relies on mutation, recombination, and selection operators, LEM employs machine learning to generate new populations. Specifically, in Machine Learning mode, a learning system seeks reasons why certain individuals in a population (or a collection of past populations) are superior to others in performing a designated class of tasks. These reasons, expressed as inductive hypotheses, are used to generate new populations. A remarkable property of LEM is that it is capable of quantum leaps (“insight jumps”) of the fitness function, unlike Darwinian-type evolution that typically proceeds through numerous slight improvements. In our early experimental studies, LEM significantly outperformed evolutionary computation methods used in the experiments, sometimes achieving speed-ups of two or more orders of magnitude in terms of the number of evolutionary steps. LEM has a potential for a wide range of applications, in particular, in such domains as complex optimization or search problems, engineering design, drug design, evolvable hardware, software engineering, economics, data mining, and automatic programming.

Classification is an important topic in data mining research. Given a set of data records, each of which belongs to one of a number of predefined classes, the classification problem is concerned with the discovery of classification rules that can allow records with unknown class membership to be correctly classified. Many algorithms have been developed to mine large data sets for classification models and they have been shown to be very effective. However, when it comes to determining the likelihood of each classification made, many of them are not designed with such purpose in mind. For this, they are not readily applicable to such problem as churn prediction. For such an application, the goal is not only to predict whether or not a subscriber would switch from one carrier to another, it is also important that the likelihood of the subscriber's doing so be predicted. The reason for this is that a carrier can then choose to provide special personalized offer and services to those subscribers who are predicted with higher likelihood to churn. Given its importance, we propose a new data mining algorithm, called data mining by evolutionary learning (DMEL), to handle classification problems of which the accuracy of each predictions made has to be estimated. In performing its tasks, DMEL searches through the possible rule space using an evolutionary approach that has the following characteristics: 1) the evolutionary process begins with the generation of an initial set of first-order rules (i.e., rules with one conjunct/condition) using a probabilistic induction technique and based on these rules, rules of higher order (two or more conjuncts) are obtained iteratively; 2) when identifying interesting rules, an objective interestingness measure is used; 3) the fitness of a ch...

Most classification algorithms receive as input a set of attributes of the classified objects. In many cases, however, the supplied set of attributes is not sufficient for creating an accurate, succinct and comprehensible representation of the target concept. To overcome this problem, researchers have proposed algorithms for automatic construction of features. The majority of these algorithms use a limited predefined set of operators for building new features. In this paper we propose a generalized and flexible framework that is capable of generating features from any given set of constructor functions. These can be domain-independent functions such as arithmetic and logic operators, or domain-dependent operators that rely on partial knowledge on the part of the user. The paper describes an algorithm which receives as input a set of classified objects, a set of attributes, and a specification for a set of constructor functions that contains their domains, ranges and properties. The algorithm produces as output a set of generated features that can be used by standard concept learners to create improved classifiers. The algorithm maintains a set of its best generated features and improves this set iteratively. During each iteration, the algorithm performs a beam search over its defined feature space and constructs new features by applying constructor functions to the members of its current feature set. The search is guided by general heuristic measures that are not confined to a specific feature representation. The algorithm was applied to a variety of classification problems and was able to generate features that were strongly related to the underlying target concepts. These features also significantly improved the accuracy achieved by standard concept learners, for a ...

Genetic algorithms (GAs) represent a class of adaptive search techniques inspired by natural evolution mechanisms. The search properties of GAs make them suitable to be used in machine learning processes and for developing fuzzy systems, the socalled genetic fuzzy systems (GFSs). In this contribution, we discuss genetics-based machine learning processes presenting the iterative rule learning approach, and a special kind of GFS, a multi-stage GFS based on the iterative rule learning approach, by learning from examples. Keywords: Fuzzy logic, fuzzy rules, genetic algorithms, machine learning. 1 Introduction Genetic Algorithms (GAs) are search algorithms that use operations found in natural genetics to guide the trek through a search space. GAs are theoretically and empirically proven to provide robust search capabilities in complex spaces, offering a valid approach to problems requiring efficient and effective searching. Much of the interest in GAs is due to the fact that they provide a...

This work presents a classification algorithm based on genetic algorithms (GAs) that discovers comprehensible IF-THEN rules, in the spirit of data mining. The proposed GA has a flexible chromosome encoding where each chromosome corresponds to a classification rule. Although the number of genes (genotype) is fixed, the number of rule conditions (phenotype) is variable. The GA also has specific mutation operators for this chromosome encoding. The algorithm was evaluated on two public domain, realworld data sets (on the medical domains of dermatology and breast cancer). 1 Introduction This work presents a system based on genetic algorithms (GAs) to perform the task of classification. The system is evaluated in two medical domains: diagnosis of dermatological diseases and prediction of recurrence of breast cancer. The use of GAs in classification is an attempt to effectively exploit the large search space usually associated with classification tasks. The GA presented here was designed ac...