A novel variational Bayesian method for variable selection in logistic regression models

Regularized Bayesian algorithms for Q-matrix inference based on saturated cognitive diagnosis modelling

Q-matrices are crucial components of cognitive diagnosis models (CDMs), which are used to provide diagnostic information and classify examinees according to their attribute profiles. The absence of an appropriate Q-matrix that correctly reflects item-attribute relationships often limits the widespread use of CDMs. Rather than relying on expert judgment for specification and post-hoc methods for validation, there has been a notable shift towards Q-matrix estimation by adopting Bayesian methods. Nevertheless, their dependency on Markov chain Monte Carlo (MCMC) estimation requires substantial computational burdens and their exploratory tendency is unscalable to large-scale settings. As a scalable and efficient alternative, this study introduces the partially confirmatory framework within a saturated CDM, where the Q-matrix can be partially defined by experts and partially inferred from data. To address the dual needs of accuracy and efficiency, the proposed framework accommodates two estimation algorithms-an MCMC algorithm and a Variational Bayesian Expectation Maximization (VBEM) algorithm. This dual-channel approach extends the model's applicability across a variety of settings. Based on simulated and real data, the proposed framework demonstrated its robustness in Q-matrix inference.

Kernel Bayesian logistic tensor decomposition with automatic rank determination for predicting multiple types of miRNA-disease associations

Identifying the association and corresponding types of miRNAs and diseases is crucial for studying the molecular mechanisms of disease-related miRNAs. Compared to traditional biological experiments, computational models can not only save time and reduce costs, but also discover potential associations on a large scale. Although some computational models based on tensor decomposition have been proposed, these models usually require manual specification of numerous hyperparameters, leading to a decrease in computational efficiency and generalization ability. Additionally, these linear models struggle to analyze complex, higher-order nonlinear relationships. Based on this, we propose a novel framework, KBLTDARD, to identify potential multiple types of miRNA-disease associations. Firstly, KBLTDARD extracts information from biological networks and high-order association network, and then fuses them to obtain more precise similarities of miRNAs (diseases). Secondly, we combine logistic tensor decomposition and Bayesian methods to achieve automatic hyperparameter search by introducing sparse-induced priors of multiple latent variables, and incorporate auxiliary information to improve prediction capabilities. Finally, an efficient deterministic Bayesian inference algorithm is developed to ensure computational efficiency. Experimental results on two benchmark datasets show that KBLTDARD has better Top-1 precision, Top-1 recall, and Top-1 F1 for new type predictions, and higher AUPR, AUC, and F1 values for new triplet predictions, compared to other state-of-the-art methods. Furthermore, case studies demonstrate the efficiency of KBLTDARD in predicting multiple types of miRNA-disease associations.

Ultra-Fast Approximate Inference Using Variational Functional Mixed Models

While Bayesian functional mixed models have been shown effective to model functional data with various complex structures, their application to extremely high-dimensional data is limited due to computational challenges involved in posterior sampling. We introduce a new computational framework that enables ultra-fast approximate inference for high-dimensional data in functional form. This framework adopts parsimonious basis to represent functional observations, which facilitates efficient compression and parallel computing in basis space. Instead of performing expensive Markov chain Monte Carlo sampling, we approximate the posterior distribution using variational Bayes and adopt a fast iterative algorithm to estimate parameters of the approximate distribution. Our approach facilitates a fast multiple testing procedure in basis space, which can be used to identify significant local regions that reflect differences across groups of samples. We perform two simulation studies to assess the performance of approximate inference, and demonstrate applications of the proposed approach by using a proteomic mass spectrometry dataset and a brain imaging dataset. Supplementary materials are available online.

Credit risk assessment mechanism of personal auto loan based on PSO-XGBoost Model

As online P2P loans in automotive financing grows, there is a need to manage and control the credit risk of the personal auto loans. In this paper, the personal auto loans data sets on the Kaggle platform are used on a machine learning based credit risk assessment mechanism for personal auto loans. An integrated Smote-Tomek Link algorithm is proposed to convert the data set into a balanced data set. Then, an improved Filter-Wrapper feature selection method is presented to select credit risk assessment indexes for the loans. Combining Particle Swarm Optimization (PSO) with the eXtreme Gradient Boosting (XGBoost) model, a PSO-XGBoost model is formed to assess the credit risk of the loans. The PSO-XGBoost model is compared against the XGBoost, Random Forest, and Logistic Regression models on the standard performance evaluation indexes of accuracy, precision, ROC curve, and AUC value. The PSO-XGBoost model is found to be superior on classification performance and classification effect.

Adaptive group-regularized logistic elastic net regression

In high-dimensional data settings, additional information on the features is often available. Examples of such external information in omics research are: (i) \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$p$\end{document}-values from a previous study and (ii) omics annotation. The inclusion of this information in the analysis may enhance classification performance and feature selection but is not straightforward. We propose a group-regularized (logistic) elastic net regression method, where each penalty parameter corresponds to a group of features based on the external information. The method, termed gren, makes use of the Bayesian formulation of logistic elastic net regression to estimate both the model and penalty parameters in an approximate empirical–variational Bayes framework. Simulations and applications to three cancer genomics studies and one Alzheimer metabolomics study show that, if the partitioning of the features is informative, classification performance, and feature selection are indeed enhanced.

Influence of Weather Conditions on the Intercity Travel Mode Choice: A Case of Xi'an

To explore the influence of weather conditions on the choice of the intercity travel mode of travelers, four modes of traveler transportation were studied in Xi'an, China, in March 2019: airplane, high-speed rail, conventional train, and express bus. The individual characteristics of travelers and intercity travel activity data were obtained, and they were matched with the weather characteristics at the departure time of the travelers. The Bayesian multinomial logit regression was employed to explore the relationship between the travel mode choice and weather characteristics. The results showed that temperature, relative humidity, rainfall, wind, air quality index, and visibility had significant effects on the travel mode selection of travelers, and the addition of these variables could improve the model's predictive performance. The research results can provide a scientific decision basis for traveler flow transfer and the prediction of traffic modes choice due to the effects of climate change.