Structure-driven induction of decision tree classifiers through neural learning

Sethi, Ishwar K.; Yoo, Jae Hung

doi:10.1016/s0031-3203(97)00005-8

Cited by 25 publications

(10 citation statements)

References 23 publications

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“…Another approach is to build a fully split tree and then prune back certain leaves that are considered overly specific. An alternative to these top-down construction methods is a recently proposed approach that determines the structure of the tree first, and then determines all the splits simultaneously by optimizing a global criterion [29].…”

Section: Methods For Constructing a Decision Treementioning

confidence: 99%

The random subspace method for constructing decision forests

Ho¹

1998

IEEE Trans. Pattern Anal. Machine Intell.

5,253

490

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Much of previous attention on decision trees focuses on the splitting criteria and optimization of tree sizes. The dilemma between overfitting and achieving maximum accuracy is seldom resolved. A method to construct a decision tree based classifier is proposed that maintains highest accuracy on training data and improves on generalization accuracy as it grows in complexity. The classifier consists of multiple trees constructed systematically by pseudorandomly selecting subsets of components of the feature vector, that is, trees constructed in randomly chosen subspaces. The subspace method is compared to single-tree classifiers and other forest construction methods by experiments on publicly available datasets, where the method's superiority is demonstrated. We also discuss independence between trees in a forest and relate that to the combined classification accuracy.

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Section: Methods For Constructing a Decision Treementioning

confidence: 99%

The random subspace method for constructing decision forests

Ho¹

1998

IEEE Trans. Pattern Anal. Machine Intell.

5,253

490

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show abstract

“…Currently, the neural tree algorithms have been widely applied in various fields (e.g., Tsujino and Nishida, 1995;Sethi and Yoo, 1997;Bhattacharya and Solomatine, 2005;Maji, 2008;Etemad-Shahidi and Mahjoobi, 2009;Su et al, 2010;Selamat and Ng, 2011). This study proposes a neural-based decision tree method, which is one of the neural tree algorithms.…”

Section: Literature Reviewsmentioning

confidence: 99%

Decision Tree–Based Classifier Combined with Neural-Based Predictor for Water-Stage Forecasts in a River Basin During Typhoons: A Case Study in Taiwan

Tsai

Wei

2012

Environmental Engineering Science

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To solve the complicated problem of water-stage predictions under the interaction of upstream flows and tidal effects during typhoon attacks, this article presents a novel approach to river-stage predictions. The proposed CART-ANN model combines both the decision trees (classification and regression trees [CART]) and the artificial neural network (ANN) techniques, which comprise the multilayer perceptron (MLP) and radial basis function (RBFNN). The combined CART-ANN model involves a two-step predicting process. First, the CART stage-level classifier can classify the river stages into higher, middle, and lower levels. Then, the ANN-based water-stage predictors are employed to predict the water stages. The proposed model was applied to the Tanshui River Basin in Taiwan. The Taipei Bridge, which is close to the estuary and affected by tidal effects, was taken as the study gauge. The mean square error and the mean absolute error were used for evaluating the variance and bias performances of the models. This study makes two contributions. First, the CART-MLP and CART-RBF were modeled to predict river stages under tidal effects during typhoons, and they were compared with three benchmark models, CART, back-propagation neural network, and RBFNN. Second, the CART-RBF successfully demonstrated that it achieved more accurate prediction than CART-MLP and three benchmark models.

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“…Then, the effective features were trained and tested using the SVM to determine the level of valve leakage. To verify the SVM models, the performance of the classifiers, including the accuracy, the Cohen's kappa number, and the training time, were compared to the corresponding data from the k-nearest neighbor classifier (k-NN) (Liao et al, 2002), neural network classifier (NN) (Yu et al, 2006), naive bayes classifier (NB) (Jiang et al, 2012), and decision tree classifier (DT) (Sethi, 1997).…”

mentioning

confidence: 99%

A novel acoustic emission detection module for leakage recognition in a gas pipeline valve

Zhang²,

Tan³

et al. 2017

Process Safety and Environmental Protection

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Internal valve leakage in a natural gas pipeline seriously impairs the safe operation on pipelines, and the recognition of leakages has therefore been a major concern of the industry. In this study, a novel leakage detection scheme based on kernel principal component analysis (kernel PCA) and the support vector machine (SVM) classifier for the recognition of the leakage level is constructed. Using this approach, the acoustic signal of the leakage is obtained as the feature source using an acoustic emission (AE) sensor. The kernel PCA is used to reduce the dimensionality of the features and extract the optimal features for the classification process, and the SVM is applied to perform the recognition of the leakage levels. The performance of the classification process based on kernel PCA and the classifier are evaluated in terms of the accuracy, Cohen's kappa number and training time. The experimental results demonstrate that the intelligent recognition model based on kernel PCA and SVM classifier is very effective for recognizing the leakage level of a valve in a natural gas pipeline.The inevitable events of gas pipeline valve leakage during the gas transportation process pose serious problems to the availability, reliability, and economy of the pipeline. Thus, the possibly used safety measures focused on early detection of both small and large leakages is not only a guarantee for safer operation but also a help for reducing the costs of the industry. To monitor the valve yield condition for substantial cost savings and safer working conditions, a great number of methods have been developed. However, the currently available leakage detection methods provide little capability for the quantitative recognition of leakage levels.

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Structure-driven induction of decision tree classifiers through neural learning

Cited by 25 publications

References 23 publications

The random subspace method for constructing decision forests

The random subspace method for constructing decision forests

Decision Tree–Based Classifier Combined with Neural-Based Predictor for Water-Stage Forecasts in a River Basin During Typhoons: A Case Study in Taiwan

A novel acoustic emission detection module for leakage recognition in a gas pipeline valve

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