A Minimum-Risk Genetic Fuzzy Classifier Based on Low Quality Data

Sánchez, Luciano; Couso, Inés

doi:10.1007/978-3-642-02319-4_79

Cited by 3 publications

(2 citation statements)

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“…For removing the uncertainty of an input variable, intervals are replaced by its midpoint and fuzzy sets are replaced by their center of gravity. For removing the imprecision of an output variable, each sample is been replicated so many times as different alternatives exist, as described in [25]. Each replication is assigned a degree of importance such that the contribution of the example to the total fitness is not influenced by the number of replicas.…”

Section: Experimental Settingsmentioning

confidence: 99%

Diagnosis of dyslexia with low quality data with genetic fuzzy systems

Palacios

Sánchez

Couso

2010

International Journal of Approximate Reasoning

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For diagnosing dyslexia in early childhood, children have to solve non-writing based graphical tests. Human experts score these tests, and decide whether the children require further consideration on the basis of their marks. Applying artificial intelligence techniques for automating this scoring is a complex task with multiple sources of uncertainty. On the one hand, there are conflicts, as different experts can assign different scores to the same set of answers. On the other hand, sometimes the experts are not completely confident with their decisions and doubt between different scores. The problem is aggravated because certain symptoms are compatible with more than one disorder. In case of doubt, the experts assign an interval-valued score to the test and ask for further information about the child before diagnosing him. Having said that, exploiting the information in uncertain datasets has been recently acknowledged as a new challenge in genetic fuzzy systems. In this paper we propose using a genetic cooperative-competitive algorithm for designing a linguistically understandable, rule-based classifier that can tackle this problem. This algorithm is part of a web-based, automated pre-screening application that can be used by the parents for detecting those symptoms that advise taking the children to a psychologist for an individual examination.

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Section: Experimental Settingsmentioning

confidence: 99%

Diagnosis of dyslexia with low quality data with genetic fuzzy systems

Palacios

Sánchez

Couso

2010

International Journal of Approximate Reasoning

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“…For using a GFS with an imbalanced dataset, either we can alter the fitness function by including a cost matrix [27] or we can preprocess the data. Both techniques have already been studied in the context of GFSs: there are works that deal with the use of fuzzy classifiers for the imbalanced dataset problem [10], [34], [36], [37], [41], and others that employ a preprocessing step in order to balance the training data before the training, which has been shown to solve the problem [1], [12], [13], [14], [15].…”

Section: Introductionmentioning

confidence: 99%

Preprocessing vague imbalanced datasets and its use in genetic fuzzy classifiers

Sánchez

Couso

2010

International Conference on Fuzzy Systems

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When there is a substantial difference between the number of cases of the majority and minority classes, minimum error-based classification systems tend to overlook these last instances. This can be corrected either by preprocessing the dataset or by altering the objective function of the classifier. In this paper we analyze the first approach, in the context of genetic fuzzy systems (GFS), and in particular of those that can operate with imprecisely observed and low quality data. We will analyze the different preprocessing mechanisms of imbalanced datasets and will show the necessity of extending these for solving those problems where the data is both imprecise and imbalanced. In addition, we include a comprehensive description of a new algorithm, able to preprocess imprecise imbalanced datasets. Several real-world datasets are used to evaluate the proposal.

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