Credal-C4.5: Decision tree based on imprecise probabilities to classify noisy data

Mantas, Carlos Javier; Abellán, Joaquín

doi:10.1016/j.eswa.2014.01.017

Cited by 109 publications

(70 citation statements)

References 19 publications

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“…We compared our results with the results from [13]. Table 4 and figure 3 show the accuracy of the proposed algorithm when compared with [13]. The parameter settings are kept as in the first experiment.…”

Section: Resultsmentioning

confidence: 90%

“…So, our FA results are competitive when compared to those of other algorithms from the state of the art. Table 9 shows that the classification accuracy of FA is competitive when compared with those of the algorithms presented in [13] when applied to different datasets. The FA showed better results when compared with [13] in three out of the four tested datasets, which are: Haberman, Liver Disordes and Sonar.…”

Section: Discussionmentioning

confidence: 99%

“…Then, these results are compared with those of different algorithms from the state of the art, which are: DPSO, CPSO, LDW PSO, GA, J48, MID3 and CCDT [12]- [13].…”

Section: Resultsmentioning

confidence: 99%

“…The experimental results showed that the performance of the HS-classifier is better than the k-nearest neighbour classifier, particle swarm, genetic algorithm and imperialist competitive algorithm-based classifier. Mantas and Abellán [13] presented a modified version of C4.5, called Credal-C4.5. The modified version of C4.5 used an imprecise probability based on mathematical theory and uncertainty measures.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The proposed classifier is evaluated on five datasets. Then, it is compared with other classification techniques [12,13]. We use FA for many reasons.…”

Section: Firefly Algorithmmentioning

confidence: 99%

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Section: Resultsmentioning

confidence: 90%

Section: Discussionmentioning

confidence: 99%

“…Then, these results are compared with those of different algorithms from the state of the art, which are: DPSO, CPSO, LDW PSO, GA, J48, MID3 and CCDT [12]- [13].…”

Section: Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

“…The proposed classifier is evaluated on five datasets. Then, it is compared with other classification techniques [12,13]. We use FA for many reasons.…”

Section: Firefly Algorithmmentioning

confidence: 99%

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A Binary Classifier Based On Firefly Algorithm

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Imprecise Classification with Non-parametric Predictive Inference

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et al. 2020

Information Processing and Management of Uncertainty in Knowledge-Based Systems

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In many situations, classifiers predict a set of states of a class variable because there is no information enough to point only one state. In the data mining area, this task is known as Imprecise Classification. Decision Trees that use imprecise probabilities, also known as Credal Decision Trees (CDTs), have been adapted to this field. The adaptation proposed so far uses the Imprecise Dirichlet Model (IDM), a mathematical model of imprecise probabilities that assumes prior knowledge about the data, depending strongly on a hyperparameter. This strong dependence is solved with the Non-Parametric Predictive Inference Model (NPI-M), also based on imprecise probabilities. This model does not make any prior assumption of the data and does not have parameters. In this work, we propose a new adaptation of CDTs to Imprecise Classification based on the NPI-M. An experimental study carried out in this research shows that the adaptation with NPI-M has an equivalent performance than the one obtained with the adaptation based on the IDM with the best choice of the hyperparameter. Consequently, since the NPI-M is a non-parametric approach, it is concluded that the NPI-M is more appropriated than the IDM to be applied to the adaptation of CDTs to Imprecise Classification. KeywordsSupported by the Spanish "Ministerio de Economía y Competitividad" and by "Fondo Europeo de Desarrollo Regional" (FEDER) under Project TEC2015-69496-R.

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Credal-C4.5: Decision tree based on imprecise probabilities to classify noisy data

Cited by 109 publications

References 19 publications

A Binary Classifier Based On Firefly Algorithm

A Binary Classifier Based On Firefly Algorithm

Foundations

Imprecise Classification with Non-parametric Predictive Inference

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