Relevant bibliographies by topics / Interpretable coefficients

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Interpretable coefficients'

Author: Grafiati
Published: 18 May 2024
Last updated: 31 July 2025

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Journal articles on the topic "Interpretable coefficients"

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Lubiński, Wojciech, and Tomasz Gólczewski. "Physiologically interpretable prediction equations for spirometric indexes." Journal of Applied Physiology 108, no. 5 (2010): 1440–46. http://dx.doi.org/10.1152/japplphysiol.01211.2009.

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The need for ethnic-specific reference values of lung function variables (LFs) is acknowledged. Their estimation requires expensive and laborious examinations, and therefore additional use of results in physiology and epidemiology would be profitable. To this end, we proposed a form of prediction equations with physiologically interpretable coefficients: a baseline, the onset age (A0) and rate (S) of LF decline, and a height coefficient. The form was tested with data from healthy, nonsmoking Poles aged 18–85 yr (1,120 men, 1,625 women) who performed spirometry maneuvers according to American T
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LIPOVETSKY, STAN. "MEANINGFUL REGRESSION COEFFICIENTS BUILT BY DATA GRADIENTS." Advances in Adaptive Data Analysis 02, no. 04 (2010): 451–62. http://dx.doi.org/10.1142/s1793536910000574.

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Multiple regression's coefficients define change in the dependent variable due to a predictor's change while all other predictors are constant. Rearranging data to paired differences of observations and keeping only biggest changes yield a matrix of a single variable change, which is close to orthogonal design, so there is no impact of multicollinearity on the regression. A similar approach is used for meaningful coefficients of nonlinear regressions with coefficients of half-elasticity, elasticity, and odds' elasticity due the gradients in each predictor. In contrast to regular linear and non
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Lawless, Connor, Jayant Kalagnanam, Lam M. Nguyen, Dzung Phan, and Chandra Reddy. "Interpretable Clustering via Multi-Polytope Machines." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 7 (2022): 7309–16. http://dx.doi.org/10.1609/aaai.v36i7.20693.

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Clustering is a popular unsupervised learning tool often used to discover groups within a larger population such as customer segments, or patient subtypes. However, despite its use as a tool for subgroup discovery and description few state-of-the-art algorithms provide any rationale or description behind the clusters found. We propose a novel approach for interpretable clustering that both clusters data points and constructs polytopes around the discovered clusters to explain them. Our framework allows for additional constraints on the polytopes including ensuring that the hyperplanes construc
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Eshima, Nobuoki, Claudio Giovanni Borroni, Minoru Tabata, and Takeshi Kurosawa. "An Entropy-Based Tool to Help the Interpretation of Common-Factor Spaces in Factor Analysis." Entropy 23, no. 2 (2021): 140. http://dx.doi.org/10.3390/e23020140.

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This paper proposes a method for deriving interpretable common factors based on canonical correlation analysis applied to the vectors of common factors and manifest variables in the factor analysis model. First, an entropy-based method for measuring factor contributions is reviewed. Second, the entropy-based contribution measure of the common-factor vector is decomposed into those of canonical common factors, and it is also shown that the importance order of factors is that of their canonical correlation coefficients. Third, the method is applied to derive interpretable common factors. Numeric
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Liu, Jin, Robert A. Perera, Le Kang, Roy T. Sabo, and Robert M. Kirkpatrick. "Obtaining Interpretable Parameters From Reparameterized Longitudinal Models: Transformation Matrices Between Growth Factors in Two Parameter Spaces." Journal of Educational and Behavioral Statistics 47, no. 2 (2021): 167–201. http://dx.doi.org/10.3102/10769986211052009.

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This study proposes transformation functions and matrices between coefficients in the original and reparameterized parameter spaces for an existing linear-linear piecewise model to derive the interpretable coefficients directly related to the underlying change pattern. Additionally, the study extends the existing model to allow individual measurement occasions and investigates predictors for individual differences in change patterns. We present the proposed methods with simulation studies and a real-world data analysis. Our simulation study demonstrates that the method can generally provide an
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Bazilevskiy, Mikhail Pavlovich. "Program for Constructing Quite Interpretable Elementary and Non-elementary Quasi-linear Regression Models." Proceedings of the Institute for System Programming of the RAS 35, no. 4 (2023): 129–44. http://dx.doi.org/10.15514/ispras-2023-35(4)-7.

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A quite interpretable linear regression satisfies the following conditions: the signs of its coefficients correspond to the meaningful meaning of the factors; multicollinearity is negligible; coefficients are significant; the quality of the model approximation is high. Previously, to construct such models, estimated using the ordinary least squares, the QInter-1 program was developed. In it, according to the given initial parameters, the mixed integer 0-1 linear programming task is automatically generated, as a result of which the most informative regressors are selected. The mathematical appa
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Takada, Masaaki, Taiji Suzuki, and Hironori Fujisawa. "Independently Interpretable Lasso for Generalized Linear Models." Neural Computation 32, no. 6 (2020): 1168–221. http://dx.doi.org/10.1162/neco_a_01279.

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Sparse regularization such as [Formula: see text] regularization is a quite powerful and widely used strategy for high-dimensional learning problems. The effectiveness of sparse regularization has been supported practically and theoretically by several studies. However, one of the biggest issues in sparse regularization is that its performance is quite sensitive to correlations between features. Ordinary [Formula: see text] regularization selects variables correlated with each other under weak regularizations, which results in deterioration of not only its estimation error but also interpretab
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Yeung, Michael. "Attention U-Net ensemble for interpretable polyp and instrument segmentation." Nordic Machine Intelligence 1, no. 1 (2021): 47–49. http://dx.doi.org/10.5617/nmi.9157.

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The difficulty associated with screening and treating colorectal polyps alongside other gastrointestinal pathology presents an opportunity to incorporate computer-aided systems. This paper develops a deep learning pipeline that accurately segments colorectal polyps and various instruments used during endoscopic procedures. To improve transparency, we leverage the Attention U-Net architecture, enabling visualisation of the attention coefficients to identify salient regions. Moreover, we improve performance by incorporating transfer learning using a pre-trained encoder, together with test-time a
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Barnett, Tim, and Patricia A. Lanier. "Comparison of Alternative Response Formats for an Abbreviated Version of Rotter's Locus of Control Scale." Psychological Reports 77, no. 1 (1995): 259–64. http://dx.doi.org/10.2466/pr0.1995.77.1.259.

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The present study analyzed the factor structure of an abbreviated version of Rotter's (1966) locus of control scale. The 11-item scale was administered in both the original forced-choice format and a 4-point rating format. The data were derived from administration of the scale as part of the National Longitudinal Survey (N = 7,407). Maximum likelihood factor analysis with oblique rotation gave a three-factor solution for both the forced-choice and rating formats, but the resulting factors were not easily interpretable, and the subscales had high intercorrelations and unacceptably low reliabili
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Zhang, Wenkai, and Hengxia Gao. "Interpretable Robust Multicriteria Ranking with TODIM in Generalized Orthopair Fuzzy Settings." Spectrum of Operational Research 3, no. 1 (2025): 14–28. https://doi.org/10.31181/sor31202632.

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The endeavor to align TODIM (an acronym in Portuguese of interactive and multicriteria decision making) with prospect theory has given rise to the development of several variant methods, including power TODIM, exponential TODIM, and logarithmic TODIM. However, these existing methods fail to address high-order uncertainty within generalized orthopair fuzzy environments. To overcome this limitation, we propose an interpretable robust TODIM approach tailored for generalized orthopair fuzzy settings. First, we extend these TODIM methods to accommodate generalized orthopair fuzzy settings, integrat
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Dissertations / Theses on the topic "Interpretable coefficients"

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Gnanguenon, guesse Girault. "Modélisation et visualisation des liens entre cinétiques de variables agro-environnementales et qualité des produits dans une approche parcimonieuse et structurée." Electronic Thesis or Diss., Montpellier, 2021. http://www.theses.fr/2021MONTS139.

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L'essor de l'agriculture numérique permet de plus en plus d'observer de manière automatisée et parfois à haute fréquence des dynamiques d'élaboration de la production et de sa qualité en fonction du climat. Les données issues de ces observations dynamiques peuvent être considérées comme des données fonctionnelles. Analyser ce nouveau type de données nécessite d'étendre les outils statistiques usuels au cas fonctionnel ou d'en proposer de nouveaux.Nous avons proposé dans cette thèse une nouvelle approche (SpiceFP: Sparse and Structured Procedure to Identify Combined Effects of Functional Predic
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FICCADENTI, Valerio. "A rank-size approach to the analysis of socio-economics data." Doctoral thesis, 2018. http://hdl.handle.net/11393/251181.

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Questa tesi è volta ad investigare due importanti fenomeni, uno naturale ed uno umano. Il primo riguarda i terremoti, mentre il secondo è legato al contenuto dei discorsi ufficiali dei presidenti americani. Per il primo caso, il nostro obiettivo è quello di definire un indicatore dei danni economici causati dai terremoti, proponendo un indice calibrato su una lunga serie di magnitudo rilevate in lunghi periodi di tempo. Mentre per il caso dei discorsi presidenziali, vogliamo quantificare il loro impatto sul mercato finanziario, in particolare studiamo l’effetto che essi hanno sull’indice “Stan
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Book chapters on the topic "Interpretable coefficients"

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Sohns, J. T., D. Gond, F. Jirasek, H. Hasse, G. H. Weber, and H. Leitte. "Embedding-Space Explanations of Learned Mixture Behavior." In Proceedings of the 3rd Conference on Physical Modeling for Virtual Manufacturing Systems and Processes. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-35779-4_3.

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AbstractData-driven machine learning (ML) models are attracting increasing interest in chemical engineering and already partly outperform traditional physical simulations. Previous work in this field has mainly focused on improving the models’ statistical performance while the thereby imparted knowledge has been taken for granted. However, also the structures learned by the model during the training are fascinating yet non-trivial to assess as they are usually high-dimensional. As such, the interpretable communication of the relationship between the learned model and domain knowledge is vital
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Turbé, Hugues, Mina Bjelogrlic, Mehdi Namdar, et al. "A Lightweight and Interpretable Model to Classify Bundle Branch Blocks from ECG Signals." In Studies in Health Technology and Informatics. IOS Press, 2022. http://dx.doi.org/10.3233/shti220393.

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Automatic classification of ECG signals has been a longtime research area with large progress having been made recently. However these advances have been achieved with increasingly complex models at the expense of model’s interpretability. In this research, a new model based on multivariate autoregressive model (MAR) coefficients combined with a tree-based model to classify bundle branch blocks is proposed. The advantage of the presented approach is to build a lightweight model which combined with post-hoc interpretability can bring new insights into important cross-lead dependencies which are
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Mohanraj, G., R. K. Nadesh, D. Karthikeyan, and M. Rajkumar. "Identifying and Segmenting COVID-19-Infected Lung Images Using a Novel Hybrid-Integrated Gradient-Based Self-Refined Parallel Model." In Advances in Computational Intelligence and Robotics. IGI Global, 2025. https://doi.org/10.4018/979-8-3373-0462-5.ch020.

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In the aftermath of COVID-19, patients often experience lingering pulmonary complications, which can be effectively detected and analyzed through chest X-rays. This study uses the potent artificial intelligence technique, U-Net structure, to automatically identify and segment lung anomalies caused by COVID-19 in chest X-ray pictures. This work aims to develop an efficient and interpretable AI system that can accurately detect and monitor entities. The proposed model leverages the strength of explainable AI and applies the self-refined segment editing technique, allowing the model to better dis
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Conference papers on the topic "Interpretable coefficients"

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Luqman, Muhammad, and Muhammad Arif. "Interpretable Machine Learning Modeling for Surface Chloride Concentration Prediction in Marine Concrete." In 14th International Civil Engineering Conference. Trans Tech Publications Ltd, 2025. https://doi.org/10.4028/p-r9up7x.

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The chloride concentration (Cs) on the concrete surface is an indispensable metric for designing resilience and estimating the lifespan of concrete structures in aquatic settings. Consequently, due to Chloride action, many reinforced concrete constructions cannot reach their intended or planned life span and experience early degradation. This study utilizes three independent machine learning techniques, Extreme Gradient Boosting (XGBoost), Multi Expression Programming (MEP), and Decision Tree (DT), to forecast the concrete’s surface chloride concentration (Cs). To achieve this objective, a com
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Isehunwa, S. O., G. T. Awojinrin, and P. Oluwatayo. "Production Forecasting in Gas Lifted Wells using Interpretable Machine Learning Techniques." In SPE Nigeria Annual International Conference and Exhibition. SPE, 2024. http://dx.doi.org/10.2118/221631-ms.

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Abstract Traditional techniques, such as decline curve analysis (DCA) and material balance, are industry-accepted standards for evaluating reservoir performance and estimating developed reserves. However, while these methods are reliable for estimating well productivity under natural flow conditions, accurate evaluation of gas lifted wells can be challenging due to the additional energy injected to augment mthe natural flow. Although attempts have been made to model gas lifted wells using machine learning (ML) techniques, these models provide little or no interpretability, thereby offering no
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Zhang, R., G. S. Li, X. Z. Yao, et al. "An Interpretable Method for Formation Pressure Calculation with Embedding Mechanism." In 57th U.S. Rock Mechanics/Geomechanics Symposium. ARMA, 2023. http://dx.doi.org/10.56952/arma-2023-0094.

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ABSTRACT Formation pressure is a key parameter in the process of oilfield exploration and development. In this study, a novel deep learning model is proposed for accurate calculation of formation pressure. Particularly, making use of the long short-term memory (LSTM) network in solving sequential-data based regression problems, this paper considers the specific domain knowledge of logging and develops a self-interpretable LSTM (SI-LSTM) to calculate the formation pressure. It can dynamically capture the potential correlation between the formation pressure and input variables, and interpretabil
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Chen, Zhi-Xuan, Cheng Jin, Tian-Jing Zhang, Xiao Wu, and Liang-Jian Deng. "SpanConv: A New Convolution via Spanning Kernel Space for Lightweight Pansharpening." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/118.

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Standard convolution operations can effectively perform feature extraction and representation but result in high computational cost, largely due to the generation of the original convolution kernel corresponding to the channel dimension of the feature map, which will cause unnecessary redundancy. In this paper, we focus on kernel generation and present an interpretable span strategy, named SpanConv, for the effective construction of kernel space. Specifically, we first learn two navigated kernels with single channel as bases, then extend the two kernels by learnable coefficients, and finally s
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Tang, Tianning, Haoyu Ding, Saishuai Dai, et al. "Data Informed Model Test Design With Machine Learning – An Example in Nonlinear Wave Load on a Vertical Cylinder." In ASME 2023 42nd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/omae2023-102682.

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Abstract Model testing is common in coastal and offshore engineering. The design of such model tests is important such that the maximal information of the underlying physics can be extrapolated with a limited amount of test cases. The optimal design of experiments also requires considering the previous similar experimental results and the typical sea-states of the ocean environments. In this study, we develop a model test design strategy based on Bayesian sampling for a classic problem in ocean engineering — nonlinear wave loading on a vertical cylinder. The new experimental design strategy is
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Omer, Pareekhan. "Improving Prediction Accuracy of Lasso and Ridge Regression as an Alternative to LS Regression to Identify Variable Selection Problems." In 3rd International Conference of Mathematics and its Applications. Salahaddin University-Erbil, 2020. http://dx.doi.org/10.31972/ticma22.05.

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This paper introduces the Lasso and Ridge Regression methods, which are two popular regularization approaches. The method they give a penalty to the coefficients differs in both of them. L1 Regularization refers to Lasso linear regression, while L2 Regularization refers to Ridge regression. As we all know, regression models serve two main purposes: explanation and prediction of scientific phenomena. Where prediction accuracy will be optimized by balancing each of the bias and variance of predictions, while explanation will be gained by constructing interpretable regression models by variable s
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Wu, Jingyao, Ting Dang, Vidhyasaharan Sethu, and Eliathamby Ambikairajah. "Belief Mismatch Coefficient (BMC): A Novel Interpretable Measure of Prediction Accuracy for Ambiguous Emotion States." In 2023 11th International Conference on Affective Computing and Intelligent Interaction (ACII). IEEE, 2023. http://dx.doi.org/10.1109/acii59096.2023.10388210.

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Shao, Puheng, Zhenwu Fang, Jinxiang Wang, Zhongsheng Lin, and Guodong Yin. "Modeling and Explanation of Driver Steering Style: An Experiment under Large-Curvature Road Condition." In Human Systems Engineering and Design (IHSED 2021) Future Trends and Applications. AHFE International, 2021. http://dx.doi.org/10.54941/ahfe1001208.

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Understanding driver’s maneuver behavior is an important prerequisite for providing drivers with different levels of assistance in the collaborative driving system. Aiming at establishing a general and interpretable model of driver steering styles, 38 drivers’ data are collected by a driving simulator platform, where a U-shaped experimental scene is built. To reduce data redundancy, Principal Component Analysis (PCA) is utilized to extract key features. Vali-dated by both Elbow Method and Silhouette Coefficient, the features are classified by k-means cluster. Finally, three driving styles with
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Hossain, Md Abir, and Calvin M. Stewart. "Creep Lifetime Prediction of Alloy 617 Using Black Box Machine Learning Approach." In ASME Turbo Expo 2024: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2024. http://dx.doi.org/10.1115/gt2024-123727.

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Abstract This study explored the application of black box machine learning (ML) to build high throughput models that predict the creep response of Ni-based Alloy 617. Black Box ML refers to highly complex machine learning algorithms that generate outputs from inputs without an interpretable internal process. The rapid implementation of suitable heat of alloys into targeted service is impeded by the extended qualification process involving chemistry-processing-structure-performance assessment. The ASME B&PV Code III outlines the requirement of 10,000 hours of creep testing before each heat
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