Efficient evolution of decision trees via fully matrix-based fitness evaluation

Costa, Vinícius G.; Salcedo-Sanz, Sancho; Pedreira, Carlos E.

doi:10.1016/j.asoc.2023.111045

Cited by 4 publications

(2 citation statements)

References 38 publications

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“…Decision trees (DTs) 37 are widely used machine learning classifiers known for their simplicity and efficiency across various fields. Random forest (RF) algorithms 38 build upon DTs, employing multiple trees for training and sample prediction.…”

Section: Methodsmentioning

confidence: 99%

Precise and rapid diagnosis of lung cancer: leveraging laser-induced breakdown spectroscopy with optimized kernel methods in machine learning

Lin,

Li,

Lin

et al. 2024

J. Anal. At. Spectrom.

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Section: Methodsmentioning

confidence: 99%

Precise and rapid diagnosis of lung cancer: leveraging laser-induced breakdown spectroscopy with optimized kernel methods in machine learning

Lin,

Li,

Lin

et al. 2024

J. Anal. At. Spectrom.

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“…In the inference step, an observation follows a path from the root to one of the leaves, determined by the results of each logical test applied to the tested observation. These tests (often called "splits") can be univariate or multivariate [31].…”

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confidence: 99%

Effect of Phase Shifting on Real-Time Detection and Classification of Power Quality Disturbances

Reyes-Archundia,

Yang,

Gutiérrez Gnecchi

et al. 2024

Energies

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Power quality improvement and Power quality disturbance (PQD) detection are two significant concerns that must be addressed to ensure an efficient power distribution within the utility grid. When the process to analyze PQD is migrated to real-time platforms, the possible occurrence of a phase mismatch can affect the algorithm’s accuracy; this paper evaluates phase shifting as an additional stage in signal acquisition for detecting and classifying eight types of single power quality disturbances. According to their mathematical models, a set of disturbances was generated using an arbitrary waveform generator BK Precision 4064. The acquisition, detection, and classification stages were embedded into a BeagleBone Black. The detection stage was performed using multiresolution analysis. The feature vectors of the acquired signals were obtained from the combination of Shannon entropy and log-energy entropy. For classification purposes, four types of classifiers were trained: multilayer perceptron, K-nearest neighbors, probabilistic neural network, and decision tree. The results show that incorporating a phase-shifting stage as a preprocessing stage significantly improves the classification accuracy in all cases.

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Parallel approaches for a decision tree-based explainability algorithm

Loreti,

Visani

2024

Future Generation Computer Systems

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Efficient evolution of decision trees via fully matrix-based fitness evaluation

Cited by 4 publications

References 38 publications

Precise and rapid diagnosis of lung cancer: leveraging laser-induced breakdown spectroscopy with optimized kernel methods in machine learning

Precise and rapid diagnosis of lung cancer: leveraging laser-induced breakdown spectroscopy with optimized kernel methods in machine learning

Effect of Phase Shifting on Real-Time Detection and Classification of Power Quality Disturbances

Parallel approaches for a decision tree-based explainability algorithm

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