A Review of evolutionary Algorithms for Data Mining

Freitas, Alex A.

doi:10.1007/978-0-387-69935-6_4

Cited by 48 publications

(23 citation statements)

References 75 publications

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“…• GAs have been widely employed to solve a large number of classification problems in the most distinct contexts and application domains [1,2,5,6,8,18].…”

Section: Partially Chained Modelsmentioning

confidence: 99%

Simpler is Better

Gonçalves

Plastino

Freitas

2015

Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

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Multi-label classification (MLC) is the task of assigning multiple class labels to an object based on the features that describe the object. One of the most effective MLC methods is known as Classifier Chains (CC). This approach consists in training q binary classifiers linked in a chain, {y1 → y2 → ... → yq}, with each responsible for classifying a specific label in {l1, l2, ..., lq}. The chaining mechanism allows each individual classifier to incorporate the predictions of the previous ones as additional information at classification time. Thus, possible correlations among labels can be automatically exploited. Nevertheless, CC suffers from two important drawbacks: (i) the label ordering is decided at random, although it usually has a strong effect on predictive accuracy; (ii) all labels are inserted into the chain, although some of them might carry irrelevant information to discriminate the others. In this paper we tackle both problems at once, by proposing a novel genetic algorithm capable of searching for a single optimized label ordering, while at the same time taking into consideration the utilization of partial chains. Experiments on benchmark datasets demonstrate that our approach is able to produce models that are both simpler and more accurate.

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“…• GAs have been widely employed to solve a large number of classification problems in the most distinct contexts and application domains [1,2,5,6,8,18].…”

Section: Partially Chained Modelsmentioning

confidence: 99%

Simpler is Better

Gonçalves

Plastino

Freitas

2015

Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

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“…Evolutionary Computation has been applied to multiple and heterogeneous areas [27]. These algorithms are mainly inspired by the natural selection process.…”

Section: Genetic Algorithms For Clusteringmentioning

confidence: 99%

“…There are a lot of different sub-areas of Evolutionary Computation that can deal with the clustering problem [27], but Genetic Algorithms is the most used. In this case, the individuals or chromosomes represent clustering solutions that evolves using mutation and crossover operators, and a heuristic or fitness function which guides the search providing information to the algorithm about the quality of the solutions in the population.…”

Section: Genetic Algorithms For Clusteringmentioning

confidence: 99%

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A tutorial on manifold clustering using genetic algorithms

Menéndez

2015

2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA)

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Automatic Manifold identification is currently a challenging problem in Machine Learning. This process consists on separating a dataset blindly, according to the form defined by the data instances in the space. Data are discriminated in groups defined by their form. These approaches are usually focused on continuity-based methods where the manifold follows a continuity criterion. Currently, clustering techniques try to deal with the discrimination process, but there are a few algorithms that can generate an accurate and robust discrimination. This tutorial aims to present new different approaches, specially focused on Genetic Algorithms, which can deal with these problems.

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“…Applications of classification include credit approval, fault and medical diagnosis, etc. Evolutionary Algorithms (EAs) [2] [3] are stochastic search algorithms based on an analogy to natural evolution and inspired by the process of neo-Darwinian evolution. The motivation for applying EAs to data mining is that they are robust, adaptive search techniques that perform a global search in the solution space.…”

Section: Introductionmentioning

confidence: 99%

Bottom-up Pittsburgh approach for discovery of classification rules

Sharma¹,

Ratnoo

2014

2014 International Conference on Contemporary Computing and Informatics (IC3I)

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This paper presents bottom-up Pittsburgh approach for discovery of classification rules. Population initialization makes use of entropy as the attribute significance measure and contains variable sized organizations. Each organization contains a set of IF-THEN rules. As bottom-up approach is employed, so traditional operators are not feasible and efficient to use. Therefore, four evolutionary operators are devised for realizing the evolutionary operations performed on organizations. Bottomup Pittsburgh approach gives best set of rule having good accuracy. In experiments, the effectiveness of the proposed algorithm is evaluated by comparing the results of bottom-up Pittsburgh with and without entropy to the top-down Michigan approach with and without entropy on 10 datasets from the UCI and KEEL repository.All results show that bottom-up Pittsburgh approach achieves a higher predictive accuracy and is more consistent.

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A Review of evolutionary Algorithms for Data Mining

Cited by 48 publications

References 75 publications

Simpler is Better

Simpler is Better

A tutorial on manifold clustering using genetic algorithms

Bottom-up Pittsburgh approach for discovery of classification rules

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