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Journal articles on the topic 'Interpretable coefficients'

Author: Grafiati

Published: 18 May 2024

Last updated: 31 July 2025

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1

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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Si, Rui, Yaoyu Lin, Dongquan Yang, and Qijin Guo. "Interpretable Machine Learning Insights into the Factors Influencing Residents’ Travel Distance Distribution." ISPRS International Journal of Geo-Information 14, no. 1 (2025): 39. https://doi.org/10.3390/ijgi14010039.

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Understanding intra-urban travel patterns through quantitative analysis is crucial for effective urban planning and transportation management. In previous studies, a range of distribution functions were modeled to lay the groundwork for human mobility research. However, few studies have explored the nonlinear relationships between travel distance patterns and environmental factors. Using travel distance data from ride-hailing services, this research divides a study area into 1 × 1 km grid cells, modeling the best travel distance distribution and calculating the coefficients of each grid. A mac

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Zheng, Fanglan, Erihe, Kun Li, Jiang Tian, and Xiaojia Xiang. "A federated interpretable scorecard and its application in credit scoring." International Journal of Financial Engineering 08, no. 03 (2021): 2142009. http://dx.doi.org/10.1142/s2424786321420093.

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In this paper, we propose a vertical federated learning (VFL) structure for logistic regression with bounded constraint for the traditional scorecard, namely FL-LRBC. Under the premise of data privacy protection, FL-LRBC enables multiple agencies to jointly obtain an optimized scorecard model in a single training session. It leads to the formation of scorecard model with positive coefficients to guarantee its desirable characteristics (e.g., interpretability and robustness), while the time-consuming parameter-tuning process can be avoided. Moreover, model performance in terms of both AUC and t

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Chen, Congyi. "Heat diffusion coefficient study of polymers based on interpretable machine learning." Theoretical and Natural Science 42, no. 1 (2024): 125–30. http://dx.doi.org/10.54254/2753-8818/42/20240674.

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Abstract. Polymers hold significant application value across various fields of modern society, with different application scenarios requiring specific thermal diffusivity coefficients. Finding polymer materials with targeted thermal diffusivities is crucial. However, due to the vast variety and complex structures of polymers, constructing a unified structured dataset for machine learning modeling is challenging. Although machine learning has shown great potential in materials science, it has rarely been applied to predict the heat diffusion coefficient of polymers. This paper constructs a data

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Yin, Hao, Austin R. Benson, and Johan Ugander. "Measuring directed triadic closure with closure coefficients." Network Science 8, no. 4 (2020): 551–73. http://dx.doi.org/10.1017/nws.2020.20.

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AbstractRecent work studying triadic closure in undirected graphs has drawn attention to the distinction between measures that focus on the “center” node of a wedge (i.e., length-2 path) versus measures that focus on the “initiator,” a distinction with considerable consequences. Existing measures in directed graphs, meanwhile, have all been center-focused. In this work, we propose a family of eight directed closure coefficients that measure the frequency of triadic closure in directed graphs from the perspective of the node initiating closure. The eight coefficients correspond to different lab

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Shapiro, Alexander A. "Thermodynamic Theory of Diffusion and Thermodiffusion Coefficients in Multicomponent Mixtures." Journal of Non-Equilibrium Thermodynamics 45, no. 4 (2020): 343–72. http://dx.doi.org/10.1515/jnet-2020-0006.

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AbstractTransport coefficients (like diffusion and thermodiffusion) are the key parameters to be studied in non-equilibrium thermodynamics. For practical applications, it is important to predict them based on the thermodynamic parameters of a mixture under study: pressure, temperature, composition, and thermodynamic functions, like enthalpies or chemical potentials. The current study develops a thermodynamic framework for such prediction. The theory is based on a system of physically interpretable postulates; in this respect, it is better grounded theoretically than the previously suggested mo

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Deng, Jiale, and Yanyan Shen. "Self-Interpretable Graph Learning with Sufficient and Necessary Explanations." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 10 (2024): 11749–56. http://dx.doi.org/10.1609/aaai.v38i10.29059.

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Self-interpretable graph learning methods provide insights to unveil the black-box nature of GNNs by providing predictions with built-in explanations. However, current works suffer from performance degradation compared to GNNs trained without built-in explanations. We argue the main reason is that they fail to generate explanations satisfying both sufficiency and necessity, and the biased explanations further hurt GNNs' performance. In this work, we propose a novel framework for generating SUfficient aNd NecessarY explanations (SUNNY-GNN for short) that benefit GNNs' predictions. The key idea

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Ding, Tianqi. "A Deep Learning-Based Approach for Relative Poverty Identification and Classification Prediction." International Journal of Electric Power and Energy Studies 4, no. 1 (2025): 40–45. https://doi.org/10.62051/ijepes.v4n1.05.

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By predicting and classifying relative poverty, we can spot and tell the difference between potentially impoverished groups early on. This allows for early intervention and efficient resource allocation, aiding long - term poverty governance. Given the lack of algorithmic research in relative poverty identification using multi - year data, this paper proposes the RP - DCSA model. It blends deep learning (DNN) with the interpretable SHapley Additive exPlanation (SHAP) model. The 2020 China Family Panel Studies (CFPS) survey data form the research base. Spearman correlation coefficients are appl

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Kotter, Adam, Samir Abdelrahman, Yi-Ki Jacob Wan, et al. "Improved Interpretability Without Performance Reduction in a Sepsis Prediction Risk Score." Diagnostics 15, no. 3 (2025): 307. https://doi.org/10.3390/diagnostics15030307.

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Objective: Sepsis is a life-threatening response to infection and a major cause of hospital mortality. Machine learning (ML) models have demonstrated better sepsis prediction performance than integer risk scores but are less widely used in clinical settings, in part due to lower interpretability. This study aimed to improve the interpretability of an ML-based model without reducing its performance in non-ICU sepsis prediction. Methods: A logistic regression model was trained to predict sepsis onset and then converted into a more interpretable integer point system, STEWS, using its regression c

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Kim, Ho Heon, Youngin Kim, and Yu Rang Park. "Interpretable Conditional Recurrent Neural Network for Weight Change Prediction: Algorithm Development and Validation Study." JMIR mHealth and uHealth 9, no. 3 (2021): e22183. http://dx.doi.org/10.2196/22183.

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Background In recent years, mobile-based interventions have received more attention as an alternative to on-site obesity management. Despite increased mobile interventions for obesity, there are lost opportunities to achieve better outcomes due to the lack of a predictive model using current existing longitudinal and cross-sectional health data. Noom (Noom Inc) is a mobile app that provides various lifestyle-related logs including food logging, exercise logging, and weight logging. Objective The aim of this study was to develop a weight change predictive model using an interpretable artificial

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Munkhdalai, Lkhagvadorj, Tsendsuren Munkhdalai, Pham Van Van Huy, Jang-Eui Hong, Keun Ho Ryu, and Nipon Theera-Umpon. "Neural Network-Augmented Locally Adaptive Linear Regression Model for Tabular Data." Sustainability 14, no. 22 (2022): 15273. http://dx.doi.org/10.3390/su142215273.

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Creating an interpretable model with high predictive performance is crucial in eXplainable AI (XAI) field. We introduce an interpretable neural network-based regression model for tabular data in this study. Our proposed model uses ordinary least squares (OLS) regression as a base-learner, and we re-update the parameters of our base-learner by using neural networks, which is a meta-learner in our proposed model. The meta-learner updates the regression coefficients using the confidence interval formula. We extensively compared our proposed model to other benchmark approaches on public datasets f

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Yu, Na, Yutong Deng, Shunyu Liu, Kaixuan Chen, Tongya Zheng, and Mingli Song. "Disentangled Table-Graph Representation for Interpretable Transmission Line Fault Location." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 1 (2025): 977–85. https://doi.org/10.1609/aaai.v39i1.32083.

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The fault location task in power grids is crucial for maintaining social order and ensuring public safety. However, existing methods that rely on tabular state records often neglect the intrinsic topological influences of transmission lines, resulting in a segmented approach to fault location that consists of multiple stages. In this paper, we propose an Disentangled Table-Graph representation framework, termed DTG, which integrates fault location tasks at coarse-grained line levels and fine-grained point levels within an end-to-end learning paradigm. Our innovative disentanglement strategy pr

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Huang, Yuehua, Wenfen Liu, Song Li, Ying Guo, and Wen Chen. "Interpretable Single-dimension Outlier Detection (ISOD): An Unsupervised Outlier Detection Method Based on Quantiles and Skewness Coefficients." Applied Sciences 14, no. 1 (2023): 136. http://dx.doi.org/10.3390/app14010136.

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A crucial area of study in data mining is outlier detection, particularly in the areas of network security, credit card fraud detection, industrial flaw detection, etc. Existing outlier detection algorithms, which can be divided into supervised methods, semi-supervised methods, and unsupervised methods, suffer from missing labeled data, the curse of dimensionality, low interpretability, etc. To address these issues, in this paper, we present an unsupervised outlier detection method based on quantiles and skewness coefficients called ISOD (Interpretable Single dimension Outlier Detection). ISOD

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Chkalov, A.V., and D.A. Degtyarev. "The dependence of indicators of floral similarity on the distance in railways of Nizhny Novgorod region." Indusrtial Botany 24, no. 1 (2024): 202–5. https://doi.org/10.5281/zenodo.10937746.

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The results of flora studies on 10 railways segments in the Nizhny Novgorod region are presented. An analysis of correlations between the Jaccard coefficients of floristic similarity, calculated for fractions of floras of the studied segments, and the distances between these segments showed a statistically significant correlation with the distance between the segments for a fraction of rare species, which can directly characterize the process of their dispersal. The results obtained confirm that methodology used for collecting and analysis of floristic data is logically interpretable and promi

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Ranasinghe, Nisal, Damith Senanayake, Sachith Seneviratne, Malin Premaratne, and Saman Halgamuge. "GINN-LP: A Growing Interpretable Neural Network for Discovering Multivariate Laurent Polynomial Equations." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 13 (2024): 14776–84. http://dx.doi.org/10.1609/aaai.v38i13.29396.

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Traditional machine learning is generally treated as a black-box optimization problem and does not typically produce interpretable functions that connect inputs and outputs. However, the ability to discover such interpretable functions is desirable. In this work, we propose GINN-LP, an interpretable neural network to discover the form and coefficients of the underlying equation of a dataset, when the equation is assumed to take the form of a multivariate Laurent Polynomial. This is facilitated by a new type of interpretable neural network block, named the “power-term approximator block”, consi

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Sedighi-Maman, Zahra, and Jonathan J. Heath. "An Interpretable Two-Phase Modeling Approach for Lung Cancer Survivability Prediction." Sensors 22, no. 18 (2022): 6783. http://dx.doi.org/10.3390/s22186783.

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Although lung cancer survival status and survival length predictions have primarily been studied individually, a scheme that leverages both fields in an interpretable way for physicians remains elusive. We propose a two-phase data analytic framework that is capable of classifying survival status for 0.5-, 1-, 1.5-, 2-, 2.5-, and 3-year time-points (phase I) and predicting the number of survival months within 3 years (phase II) using recent Surveillance, Epidemiology, and End Results data from 2010 to 2017. In this study, we employ three analytical models (general linear model, extreme gradient

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Heinemann, Lothar A. J. "How to Measure “Short-Term Hormonal Effects”?" Obstetrics and Gynecology International 2009 (2009): 1–5. http://dx.doi.org/10.1155/2009/459485.

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Background. Interest to assess short-term benefits or risks of sex-steroid hormone use (OC or HRT) exists for years. However, no validated scale is available to evaluate the broad array of described effects of short-term hormone use.Methods. A raw scale consisting of 43 specific items and 47 general data was developed. Surveys in Italy, Germany and Austria were performed and data analyzed by factorial analyses. The resulting new scale with 15 items underwent reliability and validity investigations.Results. The new scale consists of 15 items in 5 domains. Internal consistency reliability coeffi

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Chavan, Devang, and Shrihari Padatare. "Explainable AI for News Classification." International Journal for Research in Applied Science and Engineering Technology 12, no. 11 (2024): 2400–2408. https://doi.org/10.22214/ijraset.2024.65670.

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Abstract: The proliferation of news content across digital platforms necessitates robust and interpretable machine learning models to classify news into predefined categories effectively. This study investigates the integration of Explainable AI (XAI) techniques within the context of traditional machine learning models, including Naive Bayes, Logistic Regression, and Support Vector Machines (SVM), to achieve interpretable and accurate news classification. Utilizing the News Category Dataset, we preprocess the data to focus on the top 15 categories while addressing class imbalance challenges. M

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Westö, Johan, and Patrick J. C. May. "Describing complex cells in primary visual cortex: a comparison of context and multifilter LN models." Journal of Neurophysiology 120, no. 2 (2018): 703–19. http://dx.doi.org/10.1152/jn.00916.2017.

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Receptive field (RF) models are an important tool for deciphering neural responses to sensory stimuli. The two currently popular RF models are multifilter linear-nonlinear (LN) models and context models. Models are, however, never correct, and they rely on assumptions to keep them simple enough to be interpretable. As a consequence, different models describe different stimulus-response mappings, which may or may not be good approximations of real neural behavior. In the current study, we take up two tasks: 1) we introduce new ways to estimate context models with realistic nonlinearities, that

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Cheng (程思浩), Sihao, Yuan-Sen Ting (丁源森), Brice Ménard, and Joan Bruna. "A new approach to observational cosmology using the scattering transform." Monthly Notices of the Royal Astronomical Society 499, no. 4 (2020): 5902–14. http://dx.doi.org/10.1093/mnras/staa3165.

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ABSTRACT Parameter estimation with non-Gaussian stochastic fields is a common challenge in astrophysics and cosmology. In this paper, we advocate performing this task using the scattering transform, a statistical tool sharing ideas with convolutional neural networks (CNNs) but requiring neither training nor tuning. It generates a compact set of coefficients, which can be used as robust summary statistics for non-Gaussian information. It is especially suited for fields presenting localized structures and hierarchical clustering, such as the cosmological density field. To demonstrate its power,

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KHOSHGOFTAAR, TAGHI M., and EDWARD B. ALLEN. "LOGISTIC REGRESSION MODELING OF SOFTWARE QUALITY." International Journal of Reliability, Quality and Safety Engineering 06, no. 04 (1999): 303–17. http://dx.doi.org/10.1142/s0218539399000292.

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Reliable software is mandatory for complex mission-critical systems. Classifying modules as fault-prone, or not, is a valuable technique for guiding development processes, so that resources can be focused on those parts of a system that are most likely to have faults. Logistic regression offers advantages over other classification modeling techniques, such as interpretable coefficients. There are few prior applications of logistic regression to software quality models in the literature, and none that we know of account for prior probabilities and costs of misclassification. A contribution of t

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Ventrucci, Massimo, and Håvard Rue. "Penalized complexity priors for degrees of freedom in Bayesian P-splines." Statistical Modelling 16, no. 6 (2016): 429–53. http://dx.doi.org/10.1177/1471082x16659154.

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Bayesian penalized splines (P-splines) assume an intrinsic Gaussian Markov random field prior on the spline coefficients, conditional on a precision hyper-parameter [Formula: see text]. Prior elicitation of [Formula: see text] is difficult. To overcome this issue, we aim to building priors on an interpretable property of the model, indicating the complexity of the smooth function to be estimated. Following this idea, we propose penalized complexity (PC) priors for the number of effective degrees of freedom. We present the general ideas behind the construction of these new PC priors, describe t

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Masri, Sari, Ahmad Hasasneh, Mohammad Tami, and Chakib Tadj. "Exploring the Impact of Image-Based Audio Representations in Classification Tasks Using Vision Transformers and Explainable AI Techniques." Information 15, no. 12 (2024): 751. http://dx.doi.org/10.3390/info15120751.

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An important hurdle in medical diagnostics is the high-quality and interpretable classification of audio signals. In this study, we present an image-based representation of infant crying audio files to predict abnormal infant cries using a vision transformer and also show significant improvements in the performance and interpretability of this computer-aided tool. The use of advanced feature extraction techniques such as Gammatone Frequency Cepstral Coefficients (GFCCs) resulted in a classification accuracy of 96.33%. For other features (spectrogram and mel-spectrogram), the performance was ve

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Kirkland, Angus I., and Rüdiger R. Meyer. "“Indirect” High-Resolution Transmission Electron Microscopy: Aberration Measurement and Wavefunction Reconstruction." Microscopy and Microanalysis 10, no. 4 (2004): 401–13. http://dx.doi.org/10.1017/s1431927604040437.

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Improvements in instrumentation and image processing techniques mean that methods involving reconstruction of focal or beam-tilt series of images are now realizing the promise they have long offered. This indirect approach recovers both the phase and the modulus of the specimen exit plane wave function and can extend the interpretable resolution. However, such reconstructions require thea posterioridetermination of the objective lens aberrations, including the actual beam tilt, defocus, and twofold and threefold astigmatism. In this review, we outline the theory behind exit plane wavefunction

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Liu, Shiqi, Yuting Zhou, Xuemei Yang, Xiaoying Wang, and Junping Yin. "A Robust Automatic Epilepsy Seizure Detection Algorithm Based on Interpretable Features and Machine Learning." Electronics 13, no. 14 (2024): 2727. http://dx.doi.org/10.3390/electronics13142727.

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Epilepsy, as a serious neurological disorder, can be detected by analyzing the brain signals produced by neurons. Electroencephalogram (EEG) signals are the most important data source for monitoring these brain signals. However, these complex, noisy, nonlinear and nonstationary signals make detecting seizures become a challenging task. Feature-based seizure detection algorithms have become a dominant approach for automatic seizure detection. This study presents an algorithm for automatic seizure detection based on novel features with clinical and statistical significance. Our algorithms achiev

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LIU, JIAN, BIN MA, and MING LI. "PRIMA: PEPTIDE ROBUST IDENTIFICATION FROM MS/MS SPECTRA." Journal of Bioinformatics and Computational Biology 04, no. 01 (2006): 125–38. http://dx.doi.org/10.1142/s0219720006001746.

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In proteomics, tandem mass spectrometry is the key technology for peptide sequencing. However, partially due to the deficiency of peptide identification software, a large portion of the tandem mass spectra are discarded in almost all proteomics centers because they are not interpretable. The problem is more acute with the lower quality data from low end but more popular devices such as the ion trap instruments. In order to deal with the noisy and low quality data, this paper develops a systematic machine learning approach to construct a robust linear scoring function, whose coefficients are de

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Lowther, Aaron P., Paul Fearnhead, Matthew A. Nunes, and Kjeld Jensen. "Semi-automated simultaneous predictor selection for regression-SARIMA models." Statistics and Computing 30, no. 6 (2020): 1759–78. http://dx.doi.org/10.1007/s11222-020-09970-6.

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Abstract Deciding which predictors to use plays an integral role in deriving statistical models in a wide range of applications. Motivated by the challenges of predicting events across a telecommunications network, we propose a semi-automated, joint model-fitting and predictor selection procedure for linear regression models. Our approach can model and account for serial correlation in the regression residuals, produces sparse and interpretable models and can be used to jointly select models for a group of related responses. This is achieved through fitting linear models under constraints on t

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Angelis, Dimitrios, Filippos Sofos, Konstantinos Papastamatiou, and Theodoros E. Karakasidis. "Fluid Properties Extraction in Confined Nanochannels with Molecular Dynamics and Symbolic Regression Methods." Micromachines 14, no. 7 (2023): 1446. http://dx.doi.org/10.3390/mi14071446.

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In this paper, we propose an alternative road to calculate the transport coefficients of fluids and the slip length inside nano-conduits in a Poiseuille-like geometry. These are all computationally demanding properties that depend on dynamic, thermal, and geometrical characteristics of the implied fluid and the wall material. By introducing the genetic programming-based method of symbolic regression, we are able to derive interpretable data-based mathematical expressions based on previous molecular dynamics simulation data. Emphasis is placed on the physical interpretability of the symbolic ex

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Kume, Kenji, and Naoko Nose-Togawa. "An Adaptive Orthogonal SSA Decomposition Algorithm for a Time Series." Advances in Data Science and Adaptive Analysis 10, no. 01 (2018): 1850002. http://dx.doi.org/10.1142/s2424922x1850002x.

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Singular spectrum analysis (SSA) is a nonparametric spectral decomposition of a time series into arbitrary number of interpretable components. It involves a single parameter, window length [Formula: see text], which can be adjusted for the specific purpose of the analysis. After the decomposition of a time series, similar series are grouped to obtain the interpretable components by consulting with the [Formula: see text]-correlation matrix. To accomplish better resolution of the frequency spectrum, a larger window length [Formula: see text] is preferable and, in this case, the proper grouping

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Reivan-Ortiz, Geovanny Genaro, Gisela Pineda-Garcia, Bello León Parias, et al. "Adaptación y validación ecuatoriana de la Escala de Factores de Riesgo Asociados a los Trastornos de la Conducta Alimentaria (EFRATA)." Anales de Psicología 38, no. 2 (2022): 232–38. http://dx.doi.org/10.6018/analesps.475061.

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The objective of this study was to adapt and know the factorial structure and reliability in the Ecuadorian population of the EFRATA Scale of Risk Factors Associated with Eating Disorders. A non-probabilistic sample of 1172 participants were used (age: M = 21.99; SD = 2.49; 58.6% women and 41.4% men). The first parallel analysis study identified seven interpretable factors that explain 50% of the variance. The second confirmatory factor analysis study indicates an acceptable fit (GFI = 0.96; AGFI = 0.95; NFI = 0.94; RMR = 0.08). The reliability coefficients for Cronbach's alpha and McDonald's

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Wu, Yuanyuan, Linfei Zhang, Uzair Aslam Bhatti, and Mengxing Huang. "Interpretable Machine Learning for Personalized Medical Recommendations: A LIME-Based Approach." Diagnostics 13, no. 16 (2023): 2681. http://dx.doi.org/10.3390/diagnostics13162681.

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Chronic diseases are increasingly major threats to older persons, seriously affecting their physical health and well-being. Hospitals have accumulated a wealth of health-related data, including patients’ test reports, treatment histories, and diagnostic records, to better understand patients’ health, safety, and disease progression. Extracting relevant information from this data enables physicians to provide personalized patient-treatment recommendations. While collaborative filtering techniques and classical algorithms such as naive Bayes, logistic regression, and decision trees have had nota

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Cao, Yuan, Hefeng Wang, Lanxuan Guo, Anbing Zhang, and Xiaohu Wu. "Interpretable Machine Learning for Population Spatialization and Optimal Grid Scale Selection in Shanghai." Applied Sciences 15, no. 9 (2025): 4755. https://doi.org/10.3390/app15094755.

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Fine-scale population distribution information is crucial for applications in urban public safety, planning, and management. However, when using machine learning methods for population spatialization, issues such as data overfitting and limited interpretability need to be addressed. This study introduced a combined approach using eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanation (SHAP) to estimate population spatialization at various grid scales and interpret the key influencing factors, then we applied accuracy evaluation metrics and landscape ecology indices to identify th

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Smedema, Susan Miller, and René Marie Talbot. "Psychometric Validation of the Job Satisfaction of Persons With Disabilities Scale." Rehabilitation Research, Policy, and Education 34, no. 3 (2020): 176–89. http://dx.doi.org/10.1891/re-19-22.

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ObjectiveTo evaluate the measurement structure of the Job Satisfaction of Persons with Disabilities Scale (JSPDS) in a sample of employed U.S. Americans with disabilities.DesignA quantitative descriptive design using exploratory factor analysis (EFA) and correlational analysis.ParticipantsTwo hundred and fifty-nine individuals with disabilities who were employed at least 10 hours per week.ResultsThe EFA indicated a two-factor structure accounting for 42.99% of the total variance. The internal consistency reliability coefficients for the Integrated Work Environment and Job Quality factors were

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Ma, Xinghua, Xinyan Fang, Mingye Zou, et al. "A Trusted Lesion-assessment Network for Interpretable Diagnosis of Coronary Artery Disease in Coronary CT Angiography." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 6 (2025): 6009–17. https://doi.org/10.1609/aaai.v39i6.32642.

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Coronary Artery Disease (CAD) poses a significant threat to cardiovascular patients worldwide, underscoring the critical importance of automated CAD diagnostic technologies in clinical practice. Previous technologies for lesion assessment in Coronary CT Angiography (CCTA) images have been insufficient in terms of interpretability, resulting in solutions that lack clinical reliability in both network architecture and prediction outcomes, even when diagnoses are accurate. To address the limitation of interpretability, we introduce the Trusted Lesion-Assessment Network (TLA-Net), which provides a

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Smedema, Susan Miller, Fong Chan, Ming-Hung Wang, et al. "Psychometric Validation of the Taiwanese Version of theJob Satisfaction of Persons with Disabilities Scalein a Sample of Individuals with Poliomyelitis." Australian Journal of Rehabilitation Counselling 22, no. 1 (2016): 27–39. http://dx.doi.org/10.1017/jrc.2016.1.

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Objective: To evaluate the measurement structure of the Taiwanese Version of theJob Satisfaction of Persons with Disabilities Scale(JSPDS).Design: A quantitative descriptive research design using exploratory factor analysis (EFA).Participants: One hundred and thirty-two gainfully employed individuals from Taiwan with poliomyelitis participated in this study.Results: EFA result indicated a three-factor structure accounting for 54.1 per cent of the total variance. The internal consistency reliability coefficients for theintegrated work environment,job quality, andalienationfactors were 0.91, 0.7

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Jiang, Xiaolin, Guanqi Liu, Lifu Zhang, and Zhenpeng Hu. "Integrating Density Functional Theory Calculations and Machine Learning to Identify Conduction Band Minimum as a Descriptor for High-Efficiency Hydrogen Evolution Reaction Catalysts in Transition Metal Dichalcogenides." Catalysts 15, no. 4 (2025): 309. https://doi.org/10.3390/catal15040309.

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Identifying efficient and physically meaningful descriptors is crucial for the rational design of hydrogen evolution reaction (HER) catalysts. In this study, we systematically investigate the HER activity of transition metal dichalcogenide (TMD) monolayers by combining density functional theory (DFT) calculations and machine learning techniques. By exploring the relationship between key electronic properties, including the conduction band minimum (CBM), pz band center, and hydrogen adsorption free energy (ΔG*H), we establish a strong linear correlation between the CBM and ΔG*H, identifying the

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Lei, Minjie, and S. E. Clark. "Probing the Cold Neutral Medium through H I Emission Morphology with the Scattering Transform." Astrophysical Journal 947, no. 2 (2023): 74. http://dx.doi.org/10.3847/1538-4357/acc02a.

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Abstract Neutral hydrogen (H I) emission exhibits complex morphology that encodes rich information about the physics of the interstellar medium. We apply the scattering transform (ST) to characterize the H I emission structure via a set of compact and interpretable coefficients, and find a connection between the H I emission morphology and H I cold neutral medium (CNM) phase content. Where H I absorption measurements are unavailable, the H I phase structure is typically estimated from the emission via spectral line decomposition. Here, we present a new probe of the CNM content using measures t

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Cheng, Liangwei, Mingzhi Yan, Wenhui Zhang, Weiyan Guan, Lang Zhong, and Jianbo Xu. "Interpretable Digital Soil Organic Matter Mapping Based on Geographical Gaussian Process-Generalized Additive Model (GGP-GAM)." Agriculture 14, no. 9 (2024): 1578. http://dx.doi.org/10.3390/agriculture14091578.

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Soil organic matter (SOM) is a key soil component. Determining its spatial distribution is necessary for precision agriculture and to understand the ecosystem services that soil provides. However, field SOM studies are severely limited by time and costs. To obtain a spatially continuous distribution map of SOM content, it is necessary to conduct digital soil mapping (DSM). In addition, there is a vital need for both accuracy and interpretability in SOM mapping, which is difficult to achieve with conventional DSM models. To address the above issues, particularly mapping SOM content, a spatial c

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Quyen. "STORM SURGE FORECAST MODEL USING GENETIC PROGRAMMING." Journal of Military Science and Technology, no. 69A (November 16, 2020): 75–89. http://dx.doi.org/10.54939/1859-1043.j.mst.69a.2020.75-89.

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Stormsurge is a typical genuine fiasco coming from the ocean. Therefore, an accurate forecast of surges is a vital assignment to dodge property misfortunes and decrease the chance of tropical storm surges. Genetic Programming (GP) is an evolution-based model learning technique that can simultaneously find the functional form and the numeric coefficients for the model. Moreover, GP has been widely applied to build models for predictive problems. However, GP has seldom been applied to the problem of storm surge forecasting. In this paper, a new method to use GP for evolving models for storm surg

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Dong, Aoxiang, Andrew Starr, and Yifan Zhao. "End-to-end Identification of Autoregressive with Exogenous Input (ARX) Models Using Neural Networks." Machine Intelligence Research 22, no. 1 (2025): 117–30. https://doi.org/10.1007/s11633-024-1523-3.

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Abstract Traditional parametric system identification methods usually rely on apriori knowledge of the targeted system, which may not always be available, especially for complex systems. Although neural networks (NNs) have been increasingly adopted in system identification, most studies have failed to derive interpretable parametric models for further analysis. In this paper, we propose a novel end-to-end autoregressive with exogenous input (ARX) model identification framework using NNs. An order-wise neural network structure is introduced and trained using a multitask learning approach to sim

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Zheng, Ervine, Qi Yu, and Zhi Zheng. "Sparse Maximum Margin Learning from Multimodal Human Behavioral Patterns." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 4 (2023): 5437–45. http://dx.doi.org/10.1609/aaai.v37i4.25676.

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We propose a multimodal data fusion framework to systematically analyze human behavioral data from specialized domains that are inherently dynamic, sparse, and heterogeneous. We develop a two-tier architecture of probabilistic mixtures, where the lower tier leverages parametric distributions from the exponential family to extract significant behavioral patterns from each data modality. These patterns are then organized into a dynamic latent state space at the higher tier to fuse patterns from different modalities. In addition, our framework jointly performs pattern discovery and maximum-margin

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