Improving GP classification performance by injection of decision trees

König, Rikard; Johansson, Ulf; Löfström, Tuve; Niklasson, Lars

doi:10.1109/cec.2010.5585988

Cited by 7 publications

(4 citation statements)

References 24 publications

(25 reference statements)

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“…During the evaluation the framework achieved a high degree of prediction accuracy and a high degree of sensitivity. In order to obtain complete details about the characteristics of the framework, our achieved results were compared with the results achieved by the following hybrid models: DTiGP -Functional Tree (FT) classifier [19], C4.5+NB -Decision Tree + Naive Bayes classifier [20], GDADT -Genetic based Data Adaptation (GDA)+ Decision Tree (DT) [21], BFT -Best First Tree [22], REP -Regression Tree + Information Gain [22], GA+C4.5 -Genetic Algorithm(GA) + Decision Tree (RGDT)…”

Section: B Evaluation Of Prediction Accuracymentioning

confidence: 99%

Adaptable Web Prediction Framework for Disease Prediction Based on the Hybrid Case Based Reasoning Model

Trstenjak

Ðonko

Avdagić

2016

Eng. Technol. Appl. Sci. Res.

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Nowadays, we are witnessing the rapid development of medicine and various methods that are used for early detection of diseases. In order to make quality decisions in diagnosis and prevention of disease, various decision support systems based on machine learning methods have been introduced in the medical domain. Such systems play an increasingly important role in medical practice. This paper presents a new web framework concept for disease prediction. The proposed framework is object-oriented and enables online prediction of various diseases. The framework enables online creation of different autonomous prediction models depending on the characteristics of diseases. Prediction process in the framework is based on a hybrid Case Based Reasoning classifier. The framework was evaluated on disease datasets from public repositories. Experimental evaluation shows that the proposed framework achieved high diagnosis accuracy.

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Section: B Evaluation Of Prediction Accuracymentioning

confidence: 99%

Adaptable Web Prediction Framework for Disease Prediction Based on the Hybrid Case Based Reasoning Model

Trstenjak

Ðonko

Avdagić

2016

Eng. Technol. Appl. Sci. Res.

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“…J48 is a classifier based on tree structure representation, where each node represents a test of individual features and each level represents a class. The input dataset is partitioned by the tree based on the information gained to select the attribute, and the output is the hierarchical structure of the input [11,12,28]. The Naive Bayes (NB) classifier uses the Bayes' rule to compute the posterior probability of each class.…”

Section: Classificationmentioning

confidence: 99%

Feature Selection and Machine Learning Classification for Malware Detection

et al. 2015

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Malware is a computer security problem that can morph to evade traditional detection methods based on known signature matching. Since new malware variants contain patterns that are similar to those in observed malware, machine learning techniques can be used to identify new malware. This work presents a comparative study of several feature selection methods with four different machine learning classifiers in the context of static malware detection based on n-grams analysis. The result shows that the use of Principal Component Analysis (PCA) feature selection and Support Vector Machines (SVM) classification gives the best classification accuracy using a minimum number of features.

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“…In this variant, the population of genetic programs is enriched with decision trees induced using a standard technique, here J48. For more details see [19]. G-REX, just like J48, used Laplace corrections.…”

Section: Methodsmentioning

confidence: 99%

Evolved decision trees as conformal predictors

Johansson

König

Löfström

et al. 2013

2013 IEEE Congress on Evolutionary Computation

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Abstract-In conformal prediction, predictive models output sets of predictions with a bound on the error rate. In classification, this translates to that the probability of excluding the correct class is lower than a predefined significance level, in the long run. Since the error rate is guaranteed, the most important criterion for conformal predictors is efficiency. Efficient conformal predictors minimize the number of elements in the output prediction sets, thus producing more informative predictions. This paper presents one of the first comprehensive studies where evolutionary algorithms are used to build conformal predictors. More specifically, decision trees evolved using genetic programming are evaluated as conformal predictors. In the experiments, the evolved trees are compared to decision trees induced using standard machine learning techniques on 33 publicly available benchmark data sets, with regard to predictive performance and efficiency. The results show that the evolved trees are generally more accurate, and the corresponding conformal predictors more efficient, than their induced counterparts. One important result is that the probability estimates of decision trees when used as conformal predictors should be smoothed, here using the Laplace correction. Finally, using the more discriminating Brier score instead of accuracy as the optimization criterion produced the most efficient conformal predictions.

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Improving GP classification performance by injection of decision trees

Cited by 7 publications

References 24 publications

Adaptable Web Prediction Framework for Disease Prediction Based on the Hybrid Case Based Reasoning Model

Adaptable Web Prediction Framework for Disease Prediction Based on the Hybrid Case Based Reasoning Model

Feature Selection and Machine Learning Classification for Malware Detection

Evolved decision trees as conformal predictors

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