Derivation of monotone decision models from noisy data

Daniels, Hennie; Velikova, Marina

doi:10.1109/tsmcc.2005.855493

Cited by 38 publications

(24 citation statements)

References 6 publications

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“…• Non-Monotonicity Index (NMI) 41 , defined as the number of clash-pairs divided by the total number of pairs of examples in the predictions made by an algorithm:…”

Section: Experimental Methodologymentioning

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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“…• Non-Monotonicity Index (NMI) 41 , defined as the number of clash-pairs divided by the total number of pairs of examples in the predictions made by an algorithm:…”

Section: Experimental Methodologymentioning

confidence: 99%

Hyperrectangles Selection for Monotonic Classification by Using Evolutionary Algorithms

García¹,

AlBar²,

Aljohani³

et al. 2016

IJCIS

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“…We do this by taking into account all optimal relabelings, and not just one of them as was done in previous studies, such as [11] and [3]. 2) To show the variability in predictive performance with respect to different optimal relabelings of the same data set.…”

Section: Methodsmentioning

confidence: 99%

Monotone Relabeling in Ordinal Classification

Feelders

2010

2010 IEEE International Conference on Data Mining

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Abstract-In many applications of data mining we know beforehand that the response variable should be increasing (or decreasing) in the attributes. Such relations between response and attributes are called monotone. In this paper we present a new algorithm to compute an optimal monotone classification of a data set for convex loss functions. Moreover, we show how the algorithm can be extended to compute all optimal monotone classifications with little additional effort. Monotone relabeling is useful for at least two reasons. Firstly, models trained on relabeled data sets often have better predictive performance than models trained on the original data. Secondly, relabeling is an important building block for the construction of monotone classifiers. We apply the new algorithm to investigate the effect on the prediction error of relabeling the training sample for k nearest neighbour classification and classification trees. In contrast to previous work in this area, we consider all optimal monotone relabelings. The results show that, for small training samples, relabeling the training data results in significantly better predictive performance.

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“…We express the degree of monotonicity by computing the fraction of monotone pairs of states with respect to the total number of pairs from the interface function's domain [14]. Formalizing this into our framework, we define the feedforward interface function as I : H u → R o , where H u and R o denote the human input set and the simulated motor output set, respectively, both obtained through the discretization of the corresponding bounded intervals.…”

Section: ) Crypsis Coefficientmentioning

confidence: 99%