Decision tree matrix algorithm for detecting contextual faults in unmanned aerial vehicles

Alos, A.; Dahrouj, Zouhair

doi:10.3233/jifs-191575

Cited by 5 publications

(2 citation statements)

References 11 publications

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“…When compared to other data mining techniques, the evolution of decision trees is the most representative method that has been employed in previous research for general classification [13]. It has been confirmed that employing decision tree algorithms to illustrate problem relationships makes the information more understandable than using more standard, general statistical techniques [14].…”

Section: Decision Tree(dt)mentioning

confidence: 89%

Forecasting Bitcoin Closing Price by Four Machine Learning Algorithms

Zhang

2024

HBEM

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Bitcoin has increased in popularity as a speculative asset. Since 2013, eventually becoming the most recognizable cryptocurrency. But it's worth noting that the price of Bitcoin has a very high degree of volatility and diversity, which means the ability to estimate prices accurately is crucial for making wise financial decisions. Although recent research has implemented machine learning to predict Bitcoin prices with greater precision, such as Long short-term memory (LSTM), few have focused on traditional machine learning methods. In this article, the author chose a data set including nearly eight years of daily bitcoin price data for closing price prediction. Four different machine learning algorithms were used simultaneously: the Linear Regression (LR), the Decision Tree (DT) and the Random Forest (RF). An artificial neural network, the Multilayer Perceptron (MLP) was also used in this study. The author altered parameter values using the cross-validation method before creating the models in order to get more precise predictions. Finally, Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and R-squared are used as indicators to assess the outcomes from each model. The study's findings demonstrated that all three metrics of Linear Regression outperformed the performance of the other three models. Perhaps future research could focus more on traditional machine learning algorithms instead of going after complex models.

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Section: Decision Tree(dt)mentioning

confidence: 89%

Forecasting Bitcoin Closing Price by Four Machine Learning Algorithms

Zhang

2024

HBEM

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“…The use of the decision tree algorithm is more accurate than the traditional statistical segmentation method to have more correct analysis results. The use of the decision tree algorithm to present the problem relationship is clearer than using general traditional statistical methods [19,20].…”

Section: Decision Treementioning

confidence: 99%

Establishing a Multiple-Criteria Decision-Making Model for Stock Investment Decisions Using Data Mining Techniques

Cheng

Huang

et al. 2021

Sustainability

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This study attempts to integrate the decision tree algorithm with the Apriori algorithm to explore the relationship among financial ratio, corporate governance, and stock returns to establish a stock investment decision model. The sports and leisure related industries are employed as the research target. The data are collected and processed for generating decision tree and association rules. Based on the analysis outcome, an investment decision model is constructed for investors expecting to decrease their investment risks and further increase their profits. This stock investment decision model is one type of multiple-criteria decision-making model. This study makes three critical contributions to investors. (1) It proposes a systematical model of exploring related data through the decision tree algorithm and the Apriori algorithm to reveal the implicit investment knowledge. (2) An effective investment decision model is established and expected to provide a reference basis during stock-picking decisions. (3) The investment decision model is enhanced with implicit rules found among variables using association rules.

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Analysis of English Machine Translation Methods Based on Intelligent Fuzzy Decision Tree Algorithm

Gao¹

2023

Lecture Notes on Data Engineering and Communications Technologies

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Decision tree matrix algorithm for detecting contextual faults in unmanned aerial vehicles

Cited by 5 publications

References 11 publications

Forecasting Bitcoin Closing Price by Four Machine Learning Algorithms

Forecasting Bitcoin Closing Price by Four Machine Learning Algorithms

Establishing a Multiple-Criteria Decision-Making Model for Stock Investment Decisions Using Data Mining Techniques

Analysis of English Machine Translation Methods Based on Intelligent Fuzzy Decision Tree Algorithm

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