Monotone and Partially Monotone Neural Networks

Daniels, Hennie; Velikova, Marina

doi:10.1109/tnn.2010.2044803

Cited by 145 publications

(104 citation statements)

References 12 publications

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“…In short, first, all possible comparable set generated. The number of the monotonic p The degree of monotonicity is obtained wi example, the monotonicity property between for an FIS-based RPN can be measured as foll the degree of 9) [10]. The aim sing a numerical ates an increasing value near to 0 p among data.…”

Section: E Genetic Algorithmmentioning

confidence: 99%

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Optimization of Gaussian fuzzy membership functions and evaluation of the monotonicity property of Fuzzy Inference Systems

Tay

Lim

2011

2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011)

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Abstract-In this paper, two issues relating to modeling of a monotonicity-preserving Fuzzy Inference System (FIS) are examined. The first is on designing or tuning of Gaussian Membership Functions (MFs) for a monotonic FIS. Designing Gaussian MFs for an FIS is difficult because of its spreading and curvature characteristics. In this study, the sufficient conditions are exploited, and the procedure of designing Gaussian MFs is formulated as a constrained optimization problem. The second issue is on the testing procedure for a monotonic FIS. As such, a testing procedure for a monotonic FIS model is proposed. Applicability of the proposed approach is demonstrated with a real world industrial application, i.e., Failure Mode and Effect Analysis. The results obtained are analysis and discussed. The outcomes show that the proposed approach is useful in designing a monotonicity-preserving FIS model.

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Section: E Genetic Algorithmmentioning

confidence: 99%

“…From the literature, a monotonicity measure of data in neural network modeling was introduced by Daniels and Velikova [10]. However, the monotonicity measure in FIS modeling receives little attention.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Gaussian fuzzy membership functions and evaluation of the monotonicity property of Fuzzy Inference Systems

Tay

Lim

2011

2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011)

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“…Several monotonic classification approaches have been proposed in the specialized literature. They include classification trees and rule induction [9,10,11,12,13,14], neural networks [15,16], instance-based learning [4,17,18] and hybridizations [19,20]. Some of them require the training set to be purely monotone to work correctly, such as the MKNN classifier [18].…”

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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“…The first one is to preprocess the data 6 in order to "monotonize" the data set 7 , rejecting the examples that violate the monotonic restrictions or selecting features to improve classification performance and avoid overfitting 8,9 ; and the second one is to force learning only monotone classification functions. Proposals of this type are: classification trees and rule induction 10,11,12,13 , neural networks 14 and instance-based learning 15,16,17 .…”

Section: Introductionmentioning

confidence: 99%

Hyperrectangles Selection for Monotonic Classification by Using Evolutionary Algorithms

García¹,

AlBar²,

Aljohani³

et al. 2016

IJCIS

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In supervised learning, some real problems require the response attribute to represent ordinal values that should increase with some of the explaining attributes. They are called classification problems with monotonicity constraints. Hyperrectangles can be viewed as storing objects in R n which can be used to learn concepts combining instance-based classification with the axis-parallel rectangle mainly used in rule induction systems. This hybrid paradigm is known as nested generalized exemplar learning. In this paper, we propose the selection of the most effective hyperrectangles by means of evolutionary algorithms to tackle monotonic classification. The model proposed is compared through an exhaustive experimental analysis involving a large number of data sets coming from real classification and regression problems. The results reported show that our evolutionary proposal outperforms other instance-based and rule learning models, such as OLM, OSDL, k-NN and MID; in accuracy and mean absolute error, requiring a fewer number of hyperrectangles.

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Monotone and Partially Monotone Neural Networks

Cited by 145 publications

References 12 publications

Optimization of Gaussian fuzzy membership functions and evaluation of the monotonicity property of Fuzzy Inference Systems

Optimization of Gaussian fuzzy membership functions and evaluation of the monotonicity property of Fuzzy Inference Systems

Label noise filtering techniques to improve monotonic classification

Hyperrectangles Selection for Monotonic Classification by Using Evolutionary Algorithms

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