Model Selection Algorithm Research Articles

The Improved EMS Algorithm for Latent Variable Selection in M3PL Model

One of the main concerns in multidimensional item response theory (MIRT) is to detect the relationship between items and latent traits, which can be treated as a latent variable selection problem. An attractive method for latent variable selection in multidimensional 2-parameter logistic (M2PL) model is to minimize the observed Bayesian information criterion (BIC) by the expectation model selection (EMS) algorithm. The EMS algorithm extends the EM algorithm and allows the updates of the model (e.g., the loading structure in MIRT) in the iterations along with the parameters under the model. As an extension of the M2PL model, the multidimensional 3-parameter logistic (M3PL) model introduces an additional guessing parameter which makes the latent variable selection more challenging. In this paper, a well-designed EMS algorithm, named improved EMS (IEMS), is proposed to accurately and efficiently detect the underlying true loading structure in the M3PL model, which also works for the M2PL model. In simulation studies, we compare the IEMS algorithm with several state-of-art methods and the IEMS is of competitiveness in terms of model recovery, estimation precision, and computational efficiency. The IEMS algorithm is illustrated by its application to two real data sets.

6999 Utilizing Machine Learning for One-Step Diagnosis Of Adrenal Diseases: Integrating Clinical, Serum Steroid, And Body Composition Data

Abstract Disclosure: S. Park: None. J. Noh: None. J. Kim: None. T. Kim: None. H. Seo: None. J. Choo: None. M. Choi: None. J. Kim: None. Background: With the increasing incidence of adrenal incidentalomas, the precise diagnosis of subtypes, such as mild autonomous cortisol secretion (MACS), adrenal Cushing’s syndrome (ACS), primary aldosteronism (PA), pheochromocytoma and paraganglioma (PPGL), or nonfunctioning adrenal adenomas (NFA) is becoming more critical. However, the process of making accurate diagnoses remains intricate and challenging. This study aims to explore a streamlined, one-step method for diagnosing adrenal nodules, employing a machine learning (ML) model that integrates clinical data (CL), serum steroid profiles (SSP), and computed tomography-based body composition data (BC). Methods: We utilized ML models for subtyping adrenal diseases within a cohort of 641 patients (comprising MACS=141, ACS=64, PA=265, PPGL=78, and NFA=93), incorporating 37 CL, 64 SSP, and 15 BC features. Patients were randomly split into training and test cohorts at a 4:1 ratio, with each disease proportionally represented. The ML-based pipeline involved ML model selection, outlier detection, feature selection, and re-sampling algorithm. An ML model was developed to classify each of the five distinct adrenal diseases individually. In addition, separate ML models were also developed and optimized for individual comparisons between NFA and each of the other four diseases. The performance was evaluated using balanced accuracy, sensitivity, specificity, area under the curve (AUC), and F1 score. Results: The OneVsRest Classifier exhibited the best performance in the multi-class classification of the five adrenal diseases, achieving a balanced accuracy (0.78), sensitivity (0.77), specificity (0.93), and an AUC of 0.89. In this process, 10 CL features and 39 SSP features were utilized, whereas BC did not yield significant features. Using only SSP, the ML model achieved a balanced accuracy of 0.71 and an AUC of 0.89, whereas the ML model using only CL demonstrated a relatively lower accuracy of 0.58 and an AUC of 0.82. In models comparing MACS, ACS, PA, and PPGL with NFAs, the accuracies were 0.85, 0.94, 0.78, and 0.86, respectively, with AUCs of 0.96, 0.99, 0.90, and 0.94, respectively. The ML model differentiating between NFA and the other functioning adrenal diseases exhibited an accuracy of 0.75 and an AUC of 0.79 Conclusion: We have developed a highly sensitive and specific one-step ML model for differentiating adrenal diseases using CL, SSP, and BC. This approach is anticipated to facilitate precise diagnoses of adrenal nodules, thereby enhancing patient care and improving clinical outcomes. Presentation: 6/2/2024

Identifying Brain Network Structure for an fMRI Effective Connectivity Study Using the Least Absolute Shrinkage and Selection Operator (LASSO) Method.

Background: Studying causality relationships between different brain regions using the fMRI method has attracted great attention. To investigate causality relationships between different brain regions, we need to identify both the brain network structure and the influence magnitude. Most current methods concentrate on magnitude estimation, but not on identifying the connection or structure of the network. To address this problem, we proposed a nonlinear system identification method, in which a polynomial kernel was adopted to approximate the relation between the system inputs and outputs. However, this method has an overfitting problem for modelling the input-output relation if we apply the method to model the brain network directly. Methods: To overcome this limitation, this study applied the least absolute shrinkage and selection operator (LASSO) model selection method to identify both brain region networks and the connection strength (system coefficients). From these coefficients, the causality influence is derived from the identified structure. The method was verified based on the human visual cortex with phase-encoded designs. The functional data were pre-processed with motion correction. The visual cortex brain regions were defined based on a retinotopic mapping method. An eight-connection visual system network was adopted to validate the method. The proposed method was able to identify both the connected visual networks and associated coefficients from the LASSO model selection. Results: The result showed that this method can be applied to identify both network structures and associated causalities between different brain regions. Conclusions: System identification with LASSO model selection algorithm is a powerful approach for fMRI effective connectivity study.

Redefining Spectral Data Analysis with Immersive Analytics: Exploring Domain-Shifted Model Spaces for Optimal Model Selection.

Modern developments in autonomous chemometric machine learning technology strive to relinquish the need for human intervention. However, such algorithms developed and used in chemometric multivariate calibration and classification applications exclude crucial expert insight when difficult and safety-critical analysis situations arise, e.g., spectral-based medical decisions such as noninvasively determining if a biopsy is cancerous. The prediction accuracy and interpolation capabilities of autonomous methods for new samples depend on the quality and scope of their training (calibration) data. Specifically, analysis patterns within target data not captured by the training data will produce undesirable outcomes. Alternatively, using an immersive analytic approach allows insertion of human expert judgment at key machine learning algorithm junctures forming a sensemaking process performed in cooperation with a computer. The capacity of immersive virtual reality (IVR) environments to render human comprehensible three-dimensional space simulating real-world encounters, suggests its suitability as a hybrid immersive human-computer interface for data analysis tasks. Using IVR maximizes human senses to capitalize on our instinctual perception of the physical environment, thereby leveraging our innate ability to recognize patterns and visualize thresholds crucial to reducing erroneous outcomes. In this first use of IVR as an immersive analytic tool for spectral data, we examine an integrated IVR real-time model selection algorithm for a recent model updating method that adapts a model from the original calibration domain to predict samples from shifted target domains. Using near-infrared data, analyte prediction errors from IVR-selected models are reduced compared to errors using an established autonomous model selection approach. Results demonstrate the viability of IVR as a human data analysis interface for spectral data analysis including classification problems.

LDAPrototype: a model selection algorithm to improve reliability of latent Dirichlet allocation

Latent Dirichlet allocation (LDA) is a popular method for analyzing large text corpora, but it suffers from instability due to its reliance on random initialization. This results in different outcomes for replicated runs, hindering reproducibility. To address this, we introduce LDAPrototype, a new approach for selecting the most representative LDA run from multiple replications on the same dataset. LDAPrototype enhances the reliability of LDA conclusions by ensuring greater similarity between replications compared to traditional LDA runs or models chosen based on perplexity or NPMI. A key feature of LDAPrototype is its use of a novel model similarity measure called S-CLOP (Similarity of multiple sets by Clustering with LOcal Pruning). It is based on topic similarities, for which we compare the usage of measures like the thresholded Jaccard coefficient, cosine similarity, Jensen-Shannon divergence, and rank-biased overlap. The effectiveness of LDAPrototype is demonstrated through its application to six real datasets, including newspaper articles and tweets. The results show improved reproducibility and reliability in topic modeling outcomes. LDAPrototype’s approach is noteworthy for its practical applicability, comprehensibility, ease of implementation, and computational efficiency. Furthermore, the algorithm’s concept can be generalized to other topic modeling procedures that characterize topics through word distributions, making it a versatile tool in text data analysis.

Split-and-merge model selection of mixtures of Gaussian processes with RJMCMC

The mixture of Gaussian processes is a powerful statistical learning model that can be effectively applied to curve clustering and prediction. However, the corresponding model selection problem, that is, selecting an appropriate number of components in the mixture, is rather difficult to solve. In our previous work, we established the split-and-merge automatic model selection algorithm for mixtures of Gaussian processes along the output space under the framework of Reversible Jump Markov Chain Monte Carlo (RJMCMC), which can not only determine the number of actual Gaussian processes but also dynamically adjust the Gaussian process components to avoid dependence on parameter initialization and initial partitioning of the dataset during the parameter learning on a given dataset. In this study, we propose two algorithms: Penalized Likelihood RJMCMC and Penalized Prior RJMCMC. The former integrates a penalized term into the likelihood, while the latter incorporates a penalized term into the prior and operates within the full Bayesian inference framework, both aiming to focus more sharply on determining the number of components in the convergence process. Furthermore, we prove the geometric ergodicity of the RJMCMC algorithm for the mixture of Gaussian processes model, ensuring convergence of the posterior distribution with sufficient iterations. The experimental results further demonstrate the robustness of our PP-RJMCMC algorithm in model selection, showing superior performance compared to traditional approaches in curve classification and clustering. Additionally, the prediction performance is comparable to the EM algorithm. Although not directly explored in this study, the RJMCMC results can be used to initialize the EM algorithm, which could potentially improve prediction accuracy and accelerate computation.

Predictive performance of multi-model approaches for model-informed precision dosing of piperacillin in critically ill patients

ObjectivesPiperacillin (PIP)/tazobactam is a frequently prescribed antibiotic; however, over- or underdosing may contribute to toxicity, therapeutic failure, and development of antimicrobial resistance. An external evaluation of 24 published PIP-models demonstrated that model-informed precision dosing (MIPD) can enhance target attainment. Employing various candidate models, this study aimed to assess the predictive performance of different MIPD-approaches comparing (i) a single-model approach, (ii) a model selection algorithm (MSA) and (iii) a model averaging algorithm (MAA). MethodsPrecision, accuracy and expected target attainment, considering either initial (B1) or initial and secondary (B2) therapeutic drug monitoring (TDM)-samples per patient, were assessed in a multicentre dataset (561 patients, 11 German centres, 3654 TDM-samples). ResultsThe results demonstrated a slight superiority in predictive performance using MAA in B1, regardless of the candidate models, compared to MSA and the best single models (MAA, MSA, best single models: inaccuracy ±3%, ±10%, ±8%; imprecision: <25%, <31%, <28%; expected target attainment >77%, >71%, >73%). The inclusion of a second TDM-sample notably improved precision and target attainment for all MIPD-approaches, particularly within the context of MSA and most of the single models. The expected target attainment is maximized (up to >90%) when the TDM-sample is integrated within 24 h. ConclusionsIn conclusion, MAA streamlines MIPD by reducing the risk of selecting an inappropriate model for specific patients. Therefore, MIPD of PIP using MAA implicates further optimisation of antibiotic exposure in critically ill patients, by improving predictive performance with only one sample available for Bayesian forecasting, safety, and usability in clinical practice.

Synthesising Diverse and Discriminatory Sets of Instances using Novelty Search in Combinatorial Domains.

Gathering sufficient instance data to either train algorithm-selection models or understand algorithm footprints within an instance space can be challenging. We propose an approach to generating synthetic instances that are tailored to perform well with respect to a target algorithm belonging to a predefined portfolio but are also diverse with respect to their features. Our approach uses a novelty search algorithm with a linearly weighted fitness function that balances novelty and performance to generate a large set of diverse and discriminatory instances in a single run of the algorithm. We consider two definitions of novelty: (1) with respect to discriminatory performance within a portfolio of solvers; (2) with respect to the features of the evolved instances. We evaluate the proposed method with respect to its ability to generate diverse and discriminatory instances in two domains (knapsack and bin-packing), comparing to another well-known quality diversity method, Multi-dimensional Archive of Phenotypic Elites (MAP-Elites) and an evolutionary algorithm that only evolves for discriminatory behaviour. The results demonstrate that the novelty search method outperforms its competitors in terms of coverage of the space and its ability to generate instances that are diverse regarding the relative size of the "performance gap" between the target solver and the remaining solvers in the portfolio. Moreover, for the Knapsack domain, we also show that we are able to generate novel instances in regions of an instance space not covered by existing benchmarks using a portfolio of state-of-the-art solvers. Finally, we demonstrate that the method is robust to different portfolios of solvers (stochastic approaches, deterministic heuristics and state-of-the-art methods), thereby providing further evidence of its generality.

Beyond conjugacy for chain event graph model selection

Chain event graphs are a family of probabilistic graphical models that generalise Bayesian networks and have been successfully applied to a wide range of domains. Unlike Bayesian networks, these models can encode context-specific conditional independencies as well as asymmetric developments within the evolution of a process. More recently, new model classes belonging to the chain event graph family have been developed for modelling time-to-event data to study the temporal dynamics of a process. However, existing Bayesian model selection algorithms for chain event graphs and its variants rely on all parameters having conjugate priors. This is unrealistic for many real-world applications. In this paper, we propose a mixture modelling approach to model selection in chain event graphs that does not rely on conjugacy. Moreover, we show that this methodology is more amenable to being robustly scaled than the existing model selection algorithms used for this family. We demonstrate our techniques on simulated datasets.

AI for AI-based intrusion detection as a service: Reinforcement learning to configure models, tasks, and capacities

Intrusion Detection Systems (IDS) increasingly leverage machine learning (ML) to enhance the detection of zero-day attacks. As operational complexities increase, enterprises are turning to Intrusion Detection as a Service (IDaS), requiring advanced solutions for efficient ML model selection and resource allocation. Existing research often focuses primarily on accuracy and computational efficiency, leaving a gap in solutions that can dynamically adapt. This study introduces a novel integrated solution, Auto-IDaS, which employs advanced Reinforcement Learning (RL) techniques for real-time, adaptive management of IDS. Auto-IDaS uses the Deep Q-Network (DQN) algorithm for dynamic ML model selection, automatically adjusting configurations of IDaS in response to fluctuating network traffic conditions. Simultaneously, it utilizes the Twin Delayed Deep Deterministic (TD3) algorithm for optimizing capacity allocation, aiming to minimize computational costs while maintaining service quality. This dual approach is innovative in its use of RL to address both selection and allocation challenges within IDaS frameworks. The effectiveness of TD3 is compared against Simulated Annealing (SA), a traditional optimization technique. The results demonstrate that utilizing DQN to dynamically select the model significantly improves the reward by 0.29% to 27.04%, effectively balancing detection performance (F1 score), detection time, and computation cost. Regarding capacity allocation, TD3 accelerates decision times approximately 5×106 times faster than SA while retaining decision quality within a 10% range comparable to SA’s performance.

Data Validity Analysis Based on Reinforcement Learning for Mixed Types of Anomalies Coexistence in Intelligent Connected Vehicle (ICV)

Compared with traditional anomaly analysis, intelligent connected vehicle (ICV) data validity analysis is faced with a variety of data anomalies, including sensor anomalies, driving behavior anomalies, malicious tampering, and so on, which eventually leads to anomalies in the data. How to integrate the vehicle moving characteristics, driving style, and traffic flow conditions to provide an effective data detection method has become a new problem in the field of intelligent networked vehicles. Based on ICV data, a particle swarm optimization data validity detection algorithm (TE-PSO-SVM) was proposed by combining driving style and traffic flow theory to realize the effective detection of driving data. In addition, aiming at the problem of mixed types of anomalies in complex scenes, a model pool is constructed, and a model selection algorithm based on reinforcement learning (RLBMS) is proposed. Experiments on the real data set HighD show that RLBMS has a better detection effect in complex scenes of mixed types of anomalies.

Algorithm selection model based on fuzzy multi-criteria decision in big data information mining

Algorithm selection model based on fuzzy multi-criteria decision in big data information mining

Optimization of Adaptive Active Support Parameters for Large Deformation Prestressed Anchor Ropes in Soft Rock Tunnels Using Genetic Algorithm Based Computer Applications

The large deformation of soft rock tunnel is an important problem to be solved urgently with the development of engineering technology. In order to improve the application effect of prestressed anchor cable adaptive active support system, this paper puts forward an optimization method based on adaptive genetic algorithm, reviews the research status of prestressed anchor support in soft rock tunnel, and analyzes and evaluates the existing methods. This paper introduces the construction process of AGA model, establishes the problem description model and genetic optimization algorithm selection model, and constructs the adaptive active support model of prestressed anchor cable. The model adopts an optimization algorithm with a total of 100 iterations. The Simple Genetic Algorithm and Parallel Genetic Algorithm are selected to evaluate the anchoring force, the control effect of surrounding rock displacement, the erosion of supporting structure and the construction time by comparative test. The test results show that AGA has a good effect on the anchoring force, the minimum anchoring force is 457 kN, and the displacement of surrounding rock is controlled within 1.21-1.59mm, which has a good influence on the quality of supporting structure and the construction completion time. Finally, the application of AGA model in parameter optimization of prestressed anchor cable in soft rock tunnel is summarized, and the future research direction is put forward.

Database Native Model Selection: Harnessing Deep Neural Networks in Database Systems

The growing demand for advanced analytics beyond statistical aggregation calls for database systems that support effective model selection of deep neural networks (DNNs). However, existing model selection strategies are based on either training-based algorithms that deliver high-performing models at the expense of high computational cost, or training-free algorithms that enhance computational efficiency with reduced effectiveness. These strategies often disregard computational cost and response time Service-Level Objectives (SLOs), which are of concern to average or budget-conscious machine learning users. In addition, they lack a well-designed integration of the model selection algorithms with DBMSs, which hinders efficient in-database model selection. This paper presents TRAILS, a resource-efficient and SLO-aware in-database model selection system. To leverage the strengths of both training-free and training-based model selection, we first characterize nine state-of-the-art training-free model evaluation metrics and propose a more effective one named JacFlow, and then, restructure the conventional model selection procedure into two phases: filtering and refinement. A novel coordinator is also introduced to strike a balance between the high efficiency of train-free algorithms and the high effectiveness of training-based algorithms, ensuring high-performing model selection while adhering to target SLOs. Moreover, we incorporate the proposed algorithm into PostgreSQL to develop TRAILS, thereby both enhancing resource efficiency and reducing model selection latency. This integration establishes a foundation for declarative model definition and selection within DBMSs. Empirical results demonstrate that our TRAILS reduces model selection time and computational expenses considerably by up to 24.38x and 29.32x respectively compared to existing model selection systems.

Prompt access to outpatient care post-incarceration among adults with a history of substance use: Predisposing, enabling, and need-based factors

IntroductionAs expanded Medicaid coverage reduces financial barriers to receiving health care among formerly incarcerated adults, more information is needed to understand the factors that predict prompt use of health care after release among insured adults with a history of substance use. This study's aim was to estimate the associations between characteristics suggested by the Andersen behavioral model of health service use and measures of health care use during the immediate reentry period and in the presence of Medicaid coverage. MethodsIn this retrospective cohort study, we linked individual-level data from multiple Wisconsin agencies. The sample included individuals aged 18–64 released from a Wisconsin State Correctional Facility between April 2014 and June 2017 to a community in the state who enrolled in Medicaid within one month of release and had a history of substance use. We grouped predictors of outpatient care into variable domains within the Andersen model: predisposing- individual socio-demographic characteristics; enabling characteristics including area-level socio-economic resources, area-level health care supply, and characteristics of the incarceration and release; and need-based- pre-release health conditions. We used a model selection algorithm to select a subset of variable domains and estimated the association between the variables in these domains and two outcomes: any outpatient visit within 30 days of release from a state correctional facility, and receipt of medication for opioid use disorder within 30 days of release. ResultsThe size and sign of many of the estimated associations differed for our two outcomes. Race was associated with both outcomes, Black individuals being 12.1 p.p. (95 % CI, 8.7–15.4, P < .001) less likely than White individuals to have an outpatient visit within 30 days of release and 1.3 p.p. (95 % CI, 0.48–2.1, P = .002) less likely to receive MOUD within 30 days of release. Chronic pre-release health conditions were positively associated with the likelihood of post-release health care use. ConclusionsConditional on health insurance coverage, meaningful differences in post-incarceration outpatient care use still exist across adults leaving prison with a history of substance use. These findings can help guide the development of care transition interventions including the prioritization of subgroups that may warrant particular attention.

Loss-guided stability selection

AbstractIn modern data analysis, sparse model selection becomes inevitable once the number of predictor variables is very high. It is well-known that model selection procedures like the Lasso or Boosting tend to overfit on real data. The celebrated Stability Selection overcomes these weaknesses by aggregating models, based on subsamples of the training data, followed by choosing a stable predictor set which is usually much sparser than the predictor sets from the raw models. The standard Stability Selection is based on a global criterion, namely the per-family error rate, while additionally requiring expert knowledge to suitably configure the hyperparameters. Model selection depends on the loss function, i.e., predictor sets selected w.r.t. some particular loss function differ from those selected w.r.t. some other loss function. Therefore, we propose a Stability Selection variant which respects the chosen loss function via an additional validation step based on out-of-sample validation data, optionally enhanced with an exhaustive search strategy. Our Stability Selection variants are widely applicable and user-friendly. Moreover, our Stability Selection variants can avoid the issue of severe underfitting, which affects the original Stability Selection for noisy high-dimensional data, so our priority is not to avoid false positives at all costs but to result in a sparse stable model with which one can make predictions. Experiments where we consider both regression and binary classification with Boosting as model selection algorithm reveal a significant precision improvement compared to raw Boosting models while not suffering from any of the mentioned issues of the original Stability Selection.

A generalized expectation model selection algorithm for latent variable selection in multidimensional item response theory models

A generalized expectation model selection algorithm for latent variable selection in multidimensional item response theory models

Matrix-based vs. vector-based linear discriminant analysis: A comparison of regularized variants on multivariate time series data

Over the past two decades, matrix-based or bilinear discriminant analysis (BLDA) methods have received much attention. However, it has been reported that the traditional vector-based regularized LDA (RLDA) is still quite competitive and could outperform BLDA on some benchmark datasets. A central question is whether the superiority of the vector-based RLDA would always hold for general matrix data, or is there any type of matrix data on which BLDA would perform better than RLDA? Actually, the reported comparisons are found to suffer from two limitations: (i) the comparisons are only limited to image data, and (ii) regularized RLDA is compared with non-regularized BLDA. In this paper, we break the two limitations and investigate the central question on another type of matrix data, namely multivariate time series (MTS) data. We propose a new two-parameter regularized BLDA (RBLDA) for MTS data classification. To choose the two parameters, we develop an efficient model selection algorithm. The newly proposed RBLDA enables us to perform a fair comparison between vector-based RLDA and matrix-based RBLDA. Experiments on a number of real MTS data sets are conducted to compare RBLDA with RLDA and evaluate the proposed algorithm. The results reveal that the superiority of the vector-based RLDA does not always hold for general matrix data, and RBLDA outperforms RLDA on MTS data. Moreover, the proposed model selection algorithm is efficient, and RBLDA can produce better visualization of MTS data than RLDA.

Model selection via reweighted partial sparse recovery

In this work, we propose an iterative reweighted algorithm for model selection with a-priori information on some possible terms present in the unknown dynamics, which is able to recover the governing equations from noisy time-series data and has strong adaptability. In addition, we prove that the algorithm converges to a local minimizer with in a few steps. Through several examples, we show that the proposed algorithm is more robust to noise and less dependent on hyperparameters, compared to its non-reweighted counterpart. We also apply the algorithm to the model selection problems.

Automated discovery of interpretable hyperelastic material models for human brain tissue with EUCLID

We propose an automated computational algorithm for simultaneous model selection and parameter identification for the hyperelastic mechanical characterization of biological tissue and validate it on experimental data stemming from human brain tissue specimens. Following the motive of the recently proposed computational framework EUCLID (Efficient Unsupervised Constitutive Law Identification and Discovery) and in contrast to conventional parameter calibration methods, we construct an extensive set of candidate hyperelastic models, i.e., a model library including popular models known from the literature, and develop a computational strategy for automatically selecting a model from the library that conforms to the available experimental data while being represented as an interpretable symbolic mathematical expression. This computational strategy comprises sparse regression, i.e., a regression problem that is regularized by a sparsity promoting penalty term that filters out irrelevant models from the model library, and a clustering method for grouping together highly correlated and thus redundant features in the model library. The model selection procedure is driven by data stemming from mechanical tests under different deformation modes, i.e., uniaxial compression/tension and simple torsion. The data is acquired through conventional mechanical tests that deliver labeled one-dimensional data pairs, and thus the method can be interpreted as a supervised counterpart to the originally proposed EUCLID that is informed by full-field displacement data and global reaction forces. The proposed method is verified on synthetical data with artificial noise. In addition, we present for the first time an experimental investigation of the EUCLID framework by validating the proposed method on experimental data acquired through mechanical tests of human brain specimens, proving that the method is capable of discovering hyperelastic models that exhibit both high fitting accuracy to the data as well as concise and thus interpretable mathematical representations.