Measuring the Shattering coefficient of Decision Tree models

Mello, Rodrigo Fernandes de; Manapragada, Chaitanya; Bifet, Albert

doi:10.1016/j.eswa.2019.07.012

Cited by 11 publications

(5 citation statements)

References 17 publications

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“…Compared with other types of machine learning algorithms such as logistic regression (LR), artificial neural networks (ANN), etc, the CART does not need to establish a nonlinear model, and can intuitively make the decision for classification and extract knowledge rules based on decision tree graph. 31…”

Section: Cartmentioning

confidence: 99%

Automated Health Condition Diagnosis of in situ Wood Utility Poles Using an Intelligent Non-Destructive Evaluation (NDE) Framework

Subhani

Hoshyar

et al. 2020

Int. J. Str. Stab. Dyn.

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Wood utility poles are widely applied in power transmission and telecommunication systems in Australia. Because of a variety of external influence factors, such as fungi, termite and environmental conditions, failure of poles due to the wood degradation with time is of common occurrence with high degree uncertainty. The pole failure may result in serious consequences including both economic and public safety. Therefore, accurately and timely identifying the health condition of the utility poles is of great significance for economic and safe operation of electricity and communication networks. In this paper, a novel non-destructive evaluation (NDE) framework with advanced signal processing and artificial intelligence (AI) techniques is developed to diagnose the condition of utility pole in field. To begin with, the guided waves (GWs) generated within the pole is measured using multi-sensing technique, avoiding difficult interpretation of various wave modes which cannot be detected by only one sensor. Then, empirical mode decomposition (EMD) and principal component analysis (PCA) are employed to extract and select damage-sensitive features from the captured GW signals. Additionally, the up-to-date machine learning (ML) techniques are adopted to diagnose the health condition of the pole based on selected signal patterns. Eventually, the performance of the developed NDE framework is evaluated using the field testing data from 15 new and 24 decommissioned utility poles at the pole yard in Sydney.

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Section: Cartmentioning

confidence: 99%

Automated Health Condition Diagnosis of in situ Wood Utility Poles Using an Intelligent Non-Destructive Evaluation (NDE) Framework

Subhani

Hoshyar

et al. 2020

Int. J. Str. Stab. Dyn.

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“…The process of classifying relies on a top-down recursive system. These internal nodes branch out to leaf nodes according to the results of the attribute test, which are the final classifiers [22]. DTC structure is shown in Fig.…”

Section: Machine Learning Modelsmentioning

confidence: 99%

Lettuce life stage classification from texture attributes using machine learning estimators and feature selection processes

Lauguico

Concepcion

Alejandrino

et al. 2020

Int. J. Adv. Intell. Informatics

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Classification of lettuce life or growth stages is an effective tool for measuring the performance of an aquaponics system. It determines the balance in water nutrients, adequate temperature and lighting, other environmental factors, and the system’s productivity to sustain cultivars. This paper proposes a classification of lettuce life stages planted in an aquaponics system. The classification was done using the texture features of the leaves derived from machine vision algorithms. The attributes underwent three different feature selection processes, namely: Univariate Selection (US), Recursive Feature Elimination (RFE), and Feature Importance (FI) to determine the four most significant features from the original eight attributes. The features selected were used for training four estimators from Decision Trees Classifier (DTC), Gaussian Naïve Bayes (GNB), Stochastic Gradient Descent (SGD), and Linear Discriminant Analysis (LDA). The models trained using DTC and SGD were then optimized as they have hyperparameters for tuning. A comparative analysis among Machine Learning (ML) algorithms was conducted to identify the best-performing model with the given application. The best features were derived from US and FI as they have the same top four features using the DTC estimator optimized with the hyperparameters tuned to max depth having 5, criterion equated to ‘Gini', and splitter was set to 'Best'. The accuracy obtained from cross-validation evaluation resulted in 87.92%. Considering consistency with hold-out validation, LDA outperforms optimized DTC even with lower accuracy of 86.67%. This accuracy of LDA outperformed DTC due to its sufficient fit for generalizing the testing data on classifying lettuce growth stage.

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“…Decision Trees are a natural starting point for the study of extending inductive strategies to streaming scenarios; their simplicity allows us to compute model complexity [36], and they are highly interpretable. Concept Learning System (CLS) [29] was one of the first decision tree algorithms, published in 1966.…”

Section: Decision Trees For Batch Learningmentioning

confidence: 99%

An Eager Splitting Strategy for Online Decision Trees

Manapragada¹,

Gomes²,

Salehi³

et al. 2020

Preprint

Self Cite

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Concept Drift • Hoeffding Tree • ExplainabilityWe study the effectiveness of replacing the split strategy for the state-of-the-art online tree learner, Hoeffding Tree, with a rigorous but more eager splitting strategy. Our method, Hoeffding AnyTime Tree (HATT), uses the Hoeffding Test to determine whether the current best candidate split is superior to the current split, with the possibility of revision, while Hoeffding Tree aims to determine whether the top candidate is better than the second best and fixes it for all posterity. Our method converges to the ideal batch tree while Hoeffding Tree does not. Decision tree ensembles are widely used in practice, and in this work, we study the efficacy of HATT as a base learner for online bagging and online boosting ensembles. On UCI and synthetic streams, the success of Hoeffding AnyTime Tree in terms of prequential accuracy over Hoeffding Tree is established. HATT as a base learner component outperforms HT within a 0.05 significance level for the majority of tested ensembles on what we believe is the largest and most comprehensive set of testbenches in the online learning literature. Our results indicate that HATT is a superior alternative to Hoeffding Tree in a large number of ensemble settings.

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Measuring the Shattering coefficient of Decision Tree models

Cited by 11 publications

References 17 publications

Automated Health Condition Diagnosis of in situ Wood Utility Poles Using an Intelligent Non-Destructive Evaluation (NDE) Framework

Automated Health Condition Diagnosis of in situ Wood Utility Poles Using an Intelligent Non-Destructive Evaluation (NDE) Framework

Lettuce life stage classification from texture attributes using machine learning estimators and feature selection processes

An Eager Splitting Strategy for Online Decision Trees

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