Evolutionary isotonic separation for classification: theory and experiments

Malar, B.; Nadarajan, R.

doi:10.1007/s10115-012-0579-5

Cited by 5 publications

(2 citation statements)

References 18 publications

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“…The classification with monotonicity constraints, also known as monotonic classification [4] or isotonic classification [5], is an ordinal classification problem [6] where a monotonic restriction is present. In monotonic classification, a higher value of an attribute in an example, fixing other values, should not decrease its class assignment.…”

Section: Introductionmentioning

confidence: 99%

Label noise filtering techniques to improve monotonic classification

2019

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The monotonic ordinal classification has increased the interest of researchers and practitioners within machine learning community in the last years. In real applications, the problems with monotonicity constraints are very frequent. To construct predictive monotone models from those problems, many classifiers require as input a data set satisfying the monotonicity relationships among all samples. Changing the class labels of the data set (relabelling) is useful for this. Relabelling is assumed to be an important building block for the construction of monotone classifiers and it is proved that it can improve the predictive performance.In this paper, we will address the construction of monotone datasets considering as noise the cases that do not meet the monotonicity restrictions. For the first time in the specialized literature, we propose the use of noise filtering algorithms in a preprocessing stage with a double goal: to increase both the monotonicity index of the models and the accuracy of the predictions for different monotonic classifiers. The experiments are performed over 12 datasets coming from classification and regression problems and show that our scheme improves the prediction capabilities of the monotonic classifiers instead of being applied to original and relabeled datasets. In addition, we have included the analysis of noise filtering process in the particular case of wine quality classification to understand its effect in the predictive models generated.

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Section: Introductionmentioning

confidence: 99%

Label noise filtering techniques to improve monotonic classification

2019

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“…However, traditional SVM has its own weaknesses and strengths. In recent years, to overcome the drawbacks and improve the classification performance of standard SVM, several optimized models based on the original SVM have been proposed, including V-SVM [9], least squares SVM [10], NPSVM [11], Twin SVM [12], and nearly isotonic SVM [13]. In many classification tasks, SVM generally learns a nonlinear and high-dimensional set of samples that contains much irrelevant attribute information and noise data, which can lower the classification performance and computing efficiency of the SVM classifier.…”

Section: Introductionmentioning

confidence: 99%

An improved chaotic fruit fly optimization based on a mutation strategy for simultaneous feature selection and parameter optimization for SVM and its applications

Lou

Sun

2017

PLoS ONE

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This paper proposes a new support vector machine (SVM) optimization scheme based on an improved chaotic fly optimization algorithm (FOA) with a mutation strategy to simultaneously perform parameter setting turning for the SVM and feature selection. In the improved FOA, the chaotic particle initializes the fruit fly swarm location and replaces the expression of distance for the fruit fly to find the food source. However, the proposed mutation strategy uses two distinct generative mechanisms for new food sources at the osphresis phase, allowing the algorithm procedure to search for the optimal solution in both the whole solution space and within the local solution space containing the fruit fly swarm location. In an evaluation based on a group of ten benchmark problems, the proposed algorithm’s performance is compared with that of other well-known algorithms, and the results support the superiority of the proposed algorithm. Moreover, this algorithm is successfully applied in a SVM to perform both parameter setting turning for the SVM and feature selection to solve real-world classification problems. This method is called chaotic fruit fly optimization algorithm (CIFOA)-SVM and has been shown to be a more robust and effective optimization method than other well-known methods, particularly in terms of solving the medical diagnosis problem and the credit card problem.

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