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1. Randomization as Regularization: A Degrees of Freedom Explanation for Random Forest Success

   Mentch, Lucas ; Zhou, Siyu ( August 2020 , Journal of machine learning research)

   null (Ed.)

   Random forests remain among the most popular off-the-shelf supervised machine learning tools with a well-established track record of predictive accuracy in both regression and classification settings. Despite their empirical success as well as a bevy of recent work investigating their statistical properties, a full and satisfying explanation for their success has yet to be put forth. Here we aim to take a step forward in this direction by demonstrating that the additional randomness injected into individual trees serves as a form of implicit regularization, making random forests an ideal model in low signal-to-noise ratio (SNR) settings. Specifically, from a model-complexity perspective, we show that the mtry parameter in random forests serves much the same purpose as the shrinkage penalty in explicitly regularized regression procedures like lasso and ridge regression. To highlight this point, we design a randomized linear-model-based forward selection procedure intended as an analogue to tree-based random forests and demonstrate its surprisingly strong empirical performance. Numerous demonstrations on both real and synthetic data are provided. 

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2. Data-Driven Modeling of Seismic Energy Dissipation of Rocking Foundations Using Decision Tree-Based Ensemble Machine Learning Algorithms

   https://doi.org/10.1061/9780784484692.031  

   Gajan, Sivapalan ; Banker, Wakeley ; Bonacci, Alexander ( March 2023 , Geo-Congress 2023)

   The objective of this study is to develop data-driven predictive models for seismic energy dissipation of rocking shallow foundations during earthquake loading using decision tree-based ensemble machine learning algorithms and supervised learning technique. Data from a rocking foundation’s database consisting of dynamic base shaking experiments conducted on centrifuges and shaking tables have been used for the development of a base decision tree regression (DTR) model and four ensemble models: bagging, random forest, adaptive boosting, and gradient boosting. Based on k-fold cross-validation tests of models and mean absolute percentage errors in predictions, it is found that the overall average accuracy of all four ensemble models is improved by about 25%–37% when compared to base DTR model. Among the four ensemble models, gradient boosting and adaptive boosting models perform better than the other two models in terms of accuracy and variance in predictions for the problem considered. 

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3. Classification of aortic stenosis using conventional machine learning and deep learning methods based on multi-dimensional cardio-mechanical signals

   https://doi.org/10.1038/s41598-020-74519-6  

   Yang, Chenxi ; Ojha, Banish D. ; Aranoff, Nicole D. ; Green, Philip ; Tavassolian, Negar ( October 2020 , Scientific Reports)

   This paper introduces a study on the classification of aortic stenosis (AS) based on cardio-mechanical signals collected using non-invasive wearable inertial sensors. Measurements were taken from 21 AS patients and 13 non-AS subjects. A feature analysis framework utilizing Elastic Net was implemented to reduce the features generated by continuous wavelet transform (CWT). Performance comparisons were conducted among several machine learning (ML) algorithms, including decision tree, random forest, multi-layer perceptron neural network, and extreme gradient boosting. In addition, a two-dimensional convolutional neural network (2D-CNN) was developed using the CWT coefficients as images. The 2D-CNN was made with a custom-built architecture and a CNN based on Mobile Net via transfer learning. After the reduction of features by 95.47%, the results obtained report 0.87 on accuracy by decision tree, 0.96 by random forest, 0.91 by simple neural network, and 0.95 by XGBoost. Via the 2D-CNN framework, the transfer learning of Mobile Net shows an accuracy of 0.91, while the custom-constructed classifier reveals an accuracy of 0.89. Our results validate the effectiveness of the feature selection and classification framework. They also show a promising potential for the implementation of deep learning tools on the classification of AS.

    

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4. A comparison of different machine learning algorithms for predicting student performance in an online interactive mathematics game.

   Lee, Ji-Eun ; Jindal, Amisha ; Patki, sanika Nitin ; Gurung, Ashish ; Norum, Reilly ; Ottmar, Erin ( May 2023 , Interactive learning environments)

   This paper demonstrated how to apply Machine Learning (ML) techniques to analyze student interaction data collected in an online mathematics game. Using a data-driven approach, we examined 1) how different ML algorithms influenced the precision of middle-school students’ (N = 359) performance (i.e. posttest math knowledge scores) prediction and 2) what types of in-game features (i.e. student in-game behaviors, math anxiety, mathematical strategies) were associated with student math knowledge scores. The results indicated that the Random Forest algorithm showed the best performance (i.e. the accuracy of models, error measures) in predicting posttest math knowledge scores among the seven algorithms employed. Out of 37 features included in the model, the validity of the students’ first mathematical transformation was the most predictive of their posttest math knowledge scores. Implications for game learning analytics and supporting students’ algebraic learning are discussed based on the findings. 

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5. Predicting Trust Dynamics With Personal Characteristics

   https://doi.org/10.1177/10711813241260383  

   Chung, Hyesun ; Yang, X_Jessie ( September 2024 , Proceedings of the Human Factors and Ergonomics Society Annual Meeting)

   Previous research into trust dynamics in human-autonomy interaction has demonstrated that individuals exhibit specific patterns of trust when interacting repeatedly with automated systems. Moreover, people with different types of trust dynamics have been shown to differ across seven personal characteristic dimensions: masculinity, positive affect, extraversion, neuroticism, intellect, performance expectancy, and high expectations. In this study, we develop classification models aimed at predicting an individual’s trust dynamics type–categorized as Bayesian decision-maker, disbeliever, or oscillator–based on these key dimensions. We employed multiple classification algorithms including the random forest classifier, multinomial logistic regression, Support Vector Machine, XGBoost, and Naive Bayes, and conducted a comparative evaluation of their performance. The results indicate that personal characteristics can effectively predict the type of trust dynamics, achieving an accuracy rate of 73.1%, and a weighted average F1 score of 0.64. This study underscores the predictive power of personal traits in the context of human-autonomy interaction.

    

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