Classification with decision trees from a nonparametric predictive inference perspective

Abellán, Joaquín; Baker, Rebecca M.; Coolen, Frank P. A.; Crossman, R. H. S.; Masegosa, Andrés R.

doi:10.1016/j.csda.2013.02.009

Cited by 30 publications

(19 citation statements)

References 33 publications

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“…NPI for system reliability using the signature has also been presented, for systems consisting of only one type of components [4,5,15]. NPI has also been presented for a variety of other problems in operational research and statistics, including predictive analysis for queueing problems [17], replacement problems [24], decision making under uncertain utilities [30] and classification with decision trees using maximum entropy [1,2] (see also www.npi-statistics.com).…”

Section: Nonparametric Predictive Inference For System Failure Timementioning

confidence: 99%

“…The NPI lower probability for the first component to function, given test data (2, 1), is equal to 1/3. Then the second component is considered, conditional on the first component functioning, which combines with the test data to two out of three components observed (or assumed) to be functioning, so combined data (3,2), hence this second component will also function with NPI lower probability 2/4. Similarly, the NPI lower probability for the third component to function, conditional on functioning of the first two components in the system, so with combined data (4,3), is equal to 3/5.…”

Section: Examplementioning

confidence: 99%

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Nonparametric predictive inference for system reliability using the survival signature

Coolen

Coolen‐Maturi

Al-Nefaiee

2014

Proceedings of the Institution of Mechanical Engineers, Part O:

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Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. AbstractThe survival signature has recently been presented as an attractive concept to aid quantification of system reliability. It has similar characteristics as the system signature, which is well established, but contrary to the latter it is easily applicable to systems with multiple types of components. We present an introductory overview of the survival signature together with new results to aid computation. We develop nonparametric predictive inference for system reliability using the survival signature. The focus is on the failure time of a system, given failure times of tested components of the same types as used in the system.

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Section: Nonparametric Predictive Inference For System Failure Timementioning

confidence: 99%

Section: Examplementioning

confidence: 99%

Nonparametric predictive inference for system reliability using the survival signature

Coolen

Coolen‐Maturi

Al-Nefaiee

2014

Proceedings of the Institution of Mechanical Engineers, Part O:

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“…Another interesting RF called the fuzzy RF is proposed in [9]. As an alternative to the use of the IDM, nonparametric predictive inference has also been used successfully for imprecise probabilistic inference with decision trees [1]. Imprecise probabilities have also been used in classi…cation problems in [13,26,27].…”

Section: Related Workmentioning

confidence: 99%

“…Here w = (w 1 ; :::; w T ) is the weight vector, z = (z (1) ; :::; z (T ) ) T is the vector of T tree outputs corresponding to example x. The weights are also restricted by condition (3).…”

Section: Weighted Averagesmentioning

confidence: 99%

Imprecise weighted extensions of random forests for classification and regression

Utkin

Kovalev

Coolen

2020

Applied Soft Computing

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One of the main problems of using the random forests (RF) in classi…cation and regression tasks is a lack of su¢ cient data which fall into certain leaves of trees in order to estimate the tree predicted values. To cope with this problem, robust imprecise classi…cation and regression RF models, called the imprecise RF, are proposed. They are based on the following ideas. First, imprecision of the tree estimates is taken into account by means of imprecise statistical inference models and con…dence interval models. Secondly, we introduce weights assigned to trees or to groups of trees, which are computed in order to correct the RF estimates under condition of imprecise tree predicted values. In fact, the weights can be regarded as a robust meta-learner controlling the imprecision of estimates. Special modi…cations of loss functions to compute optimal weights for the classi…cation and regression tasks are proposed in order to simplify maximin optimization problems. As a result, simple linear and quadratic optimization problems are obtained, whose solution does not meet any di¢ culties. Various numerical examples with real datasets illustrate the proposed robust models and show outperforming results when datasets are rather small or noisy.

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“…Decision trees are particularly promising in this regard, due to their comprehensible nature that resembles the hierarchical process of human decision making. To split nodes while growing the tree, the vast majority of the oblique and univariate decision-tree induction algorithms employ different impurity-based measures [1,2]. Aside from such advantages, such as the ability to explain the decision process and low computational costs, their drawbacks, caused by the single-tree structure, cannot be ignored.…”

Section: Introductionmentioning

confidence: 99%

How to Mine Information from Each Instance to Extract an Abbreviated and Credible Logical Rule

Wang

Sun

Cao

2014

Entropy

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Decision trees are particularly promising in symbolic representation and reasoning due to their comprehensible nature, which resembles the hierarchical process of human decision making. However, their drawbacks, caused by the single-tree structure, cannot be ignored. A rigid decision path may cause the majority class to overwhelm other class when dealing with imbalanced data sets, and pruning removes not only superfluous nodes, but also subtrees. The proposed learning algorithm, flexible hybrid decision forest (FHDF), mines information implicated in each instance to form logical rules on the basis of a chain rule of local mutual information, then forms different decision tree structures and decision forests later. The most credible decision path from the decision forest can be selected to make a prediction. Furthermore, functional dependencies (FDs), which are extracted from the whole data set based on association rule analysis, perform embedded attribute selection to remove nodes rather than subtrees, thus helping to achieve different levels of knowledge representation and improve model comprehension in the framework of semi-supervised learning. Naive Bayes replaces the leaf nodes at the bottom of the tree hierarchy, where the conditional independence assumption may hold. This technique reduces the potential for overfitting and overtraining and improves the prediction quality and generalization. Experimental results on UCI data sets demonstrate the efficacy of the proposed approach.

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Classification with decision trees from a nonparametric predictive inference perspective

Cited by 30 publications

References 33 publications

Nonparametric predictive inference for system reliability using the survival signature

Nonparametric predictive inference for system reliability using the survival signature

Imprecise weighted extensions of random forests for classification and regression

How to Mine Information from Each Instance to Extract an Abbreviated and Credible Logical Rule

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