Comparison of random forest, artificial neural networks and support vector machine for intelligent diagnosis of rotating machinery

Han, Te; Jiang, Dongxiang; Zhao, Qi; Wang, Lei; Yin, Kedong

doi:10.1177/0142331217708242

Cited by 277 publications

(136 citation statements)

References 42 publications

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“…They presented a specific taxonomy of random forest and compared the existing random forest methods. Recent research found random forest to be more robust and stable than extreme learning machines, SVM, and neural networks, especially with small training sets (Han, Jiang, Zhao, Wang, & Yin, ).…”

Section: Ensemble Methodsmentioning

confidence: 99%

Ensemble learning: A survey

Sagi

Rokach

2018

WIREs Data Min & Knowl

1,960

950

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Ensemble methods are considered the state-of-the art solution for many machine learning challenges. Such methods improve the predictive performance of a single model by training multiple models and combining their predictions. This paper introduce the concept of ensemble learning, reviews traditional, novel and state-ofthe-art ensemble methods and discusses current challenges and trends in the field. This article is categorized under: Algorithmic Development > Ensemble Methods Technologies > Machine Learning Technologies > Classification K E Y W O R D S boosting, classifier combination, ensemble models, machine-learning, mixtures of experts, multiple classifier system, random forest 1 | INTRODUCTIONEnsemble learning is an umbrella term for methods that combine multiple inducers to make a decision, typically in supervised machine learning tasks. An inducer, also referred as a base-learner, is an algorithm that takes a set of labeled examples as input and produces a model (e.g., a classifier or regressor) that generalizes these examples. By using the produced model, predictions can be drawn for new unlabeled examples. An ensemble inducer can be of any type of machine learning algorithm (e.g., decision tree, neural network, linear regression model, etc.). The main premise of ensemble learning is that by combining multiple models, the errors of a single inducer will likely be compensated by other inducers, and as a result, the overall prediction performance of the ensemble would be better than that of a single inducer. Ensemble learning is usually regarded as the machine learning interpretation for the wisdom of the crowd. This concept can be illustrated through the story of Sir Francis Galton who was an English philosopher and statistician that conceived the basic concept of standard deviation and correlation. While visiting a livestock fair, Galton conducted a simple weight guessing contest. The participants were asked to guess the weight of an ox. Hundreds of people participated in this contest, but no one succeeded in guessing the weight: 1,198 pounds. Much to his surprise, Galton found that the average of all guesses came quite close to the exact weight: 1,198 pounds. In this experiment, Galton revealed the power of combining many predictions in order to obtain an accurate prediction. Ensemble methods manifest this concept in machine learning challenges, where they result in improved predictive performance compared to a single model. In addition, when the computational cost of the participating inducers is low (e.g., decision tree), ensemble models are often very efficient.

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Section: Ensemble Methodsmentioning

confidence: 99%

Ensemble learning: A survey

Sagi

Rokach

2018

WIREs Data Min & Knowl

1,960

950

View full text Add to dashboard Cite

show abstract

“…SVM have shown excellent performance with high classification accuracies when applied to datasets with limited number of training samples [30]. Artificial Neural Networks (ANN) are mathematical models that were initially developed to mimic the complex pattern of neuron interconnections in the human brain [31,32]. Presently, a lot of feed-forward neural networks models have been extensively studied in fault detection and diagnosis of mechanical systems.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Spectral Unmixing: Using Artificial Neural Networks for Linear/Non-Linear Switching

et al. 2017

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Spectral unmixing is a key process in identifying spectral signature of materials and quantifying their spatial distribution over an image. The linear model is expected to provide acceptable results when two assumptions are satisfied: (1) The mixing process should occur at macroscopic level and (2) Photons must interact with single material before reaching the sensor. However, these assumptions do not always hold and more complex nonlinear models are required. This study proposes a new hybrid method for switching between linear and nonlinear spectral unmixing of hyperspectral data based on artificial neural networks. The neural networks was trained with parameters within a window of the pixel under consideration. These parameters are computed to represent the diversity of the neighboring pixels and are based on the Spectral Angular Distance, Covariance and a non linearity parameter. The endmembers were extracted using Vertex Component Analysis while the abundances were estimated using the method identified by the neural networks (Vertex Component Analysis, Fully Constraint Least Square Method, Polynomial Post Nonlinear Mixing Model or Generalized Bilinear Model). Results show that the hybrid method performs better than each of the individual techniques with high overall accuracy, while the abundance estimation error is significantly lower than that obtained using the individual methods. Experiments on both synthetic dataset and real hyperspectral images demonstrated that the proposed hybrid switch method is efficient for solving spectral unmixing of hyperspectral images as compared to individual algorithms.

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“…Random Forest greatly improves the robustness of the classification model by maintaining flexibility and interpretability of decision trees . Random Forest is also proven to be more efficient in dealing with imbalanced data . In this study, we also investigate the choice of other algorithms such as Gradient Boosted Trees and Support Vector Machines .…”

Section: Predictive Equivalent Mutant Classification Approachmentioning

confidence: 99%

A machine learning approach for classification of equivalent mutants

Naeem

Lin

Naeem

et al. 2019

J Software Evolu Process

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Mutation testing is a fault-based technique to test the quality of test suites by inducing artificial syntactic faults or mutants in a source program. However, some mutants have the same semantics as original program and cannot be detected by any test suite input known as equivalent mutants. Equivalent mutant problem (EMP) is undecidable as it requires manual human effort to identify a mutant as equivalent or killable. The constraint-based testing (CBT) theory suggests the use of mathematical constraints which can help reveal some equivalent mutants using mutant features. In this paper, we consider three metrics of CBT theory, ie, reachability, necessity, and sufficiency to extract feature constraints from mutant programs. Constraints are extracted using program dependency graphs. Other features such as degree of significance, semantic distance, and information entropy of mutants are also extracted to build a binary classification model. Machine learning algorithms such as Random Forest, GBT, and SVM are applied under two application scenarios (split-project and cross-project) on ten Java programs to predict equivalent mutants. The analysis of the study demonstrates that that the proposed techniques not only improves the efficiency of the equivalent mutant detection but also reduces the effort required to perform it with small accuracy loss.

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Comparison of random forest, artificial neural networks and support vector machine for intelligent diagnosis of rotating machinery

Cited by 277 publications

References 42 publications

Ensemble learning: A survey

Ensemble learning: A survey

Hybrid Spectral Unmixing: Using Artificial Neural Networks for Linear/Non-Linear Switching

A machine learning approach for classification of equivalent mutants

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