The INFORMS Journal on Data Science

The INFORMS Journal on Data Science (IJDS) publishes innovative data science methodologies for decision making.

[The INFORMS Journal on Data Science @InformsJDataSci]

✍️Summary: In this work, the authors propose a kernel-based iterative regression model for enhancing time series forecasting accuracy by integrating data from multiple spatial locations.

#regression
#timeseriesforecasting
#INFORMS
#datascience

[The INFORMS Journal on Data Science @InformsJDataSci]

🔔New article alert @InformsJDataSci
🗒️Title: Spatio-Temporal Time Series Forecasting Using an Iterative Kernel-Based Regression
✏️Authors (photos are ordered based on): Ben Hen and Neta Rabin
🔗DOI: https://t.co/yFKMlkpdSO

[The INFORMS Journal on Data Science @InformsJDataSci]

✏️ Summary: In this work, the authors propose a kernel-based iterative regression model for enhancing time series forecasting accuracy by integrating data from multiple spatial locations.

#kernelbasedregression
#timeseriesforecasting
#INFORMS
#datascience

[The INFORMS Journal on Data Science @InformsJDataSci]

(2/2): simultaneously removes noninformative sensors and groups functional curves into clusters using informative sensors.

#INFORMS
#DataScience
#Clustering
#Multisensor
#FunctionalPrincipalComponentAnalysis
#GaussianMixture

[The INFORMS Journal on Data Science @InformsJDataSci]

🔥 New article @InformsJDataSci
📝 Title: Multivariate Functional Clustering with Variable Selection and Application to Sensor Data from Engineering Systems
👥 Authors (photos are based on): Zhongnan Jin, Jie Min, Yili Hong, Pang Du, Qingyu Yang

🔗 DOI: https://t.co/aUdOgnB6RL

[The INFORMS Journal on Data Science @InformsJDataSci]

🔍 Summary (1/2): Multisensor data that track system operating behaviors are widely available in engineering systems. Yet, the possible presence of sensors whose data contain irrelevant information poses a challenge. The authors propose a functional data clustering method that

[The INFORMS Journal on Data Science @InformsJDataSci]

#INFORMS #datascience #physicsinformedstatisticallearning #spatiotemporalmodel #advectiondiffusionprocesses #aerosolopticaldepth #wildfires #SolarEnergy #remotesensing #DataFusion

[The INFORMS Journal on Data Science @InformsJDataSci]

🔔 New article @InformsJDataSci
📝"A Statistical Model for Multisource Remote-Sensing Data Streams of Wildfire Aerosols Optimal Depth"
✍️ Authors (photos are ordered based on): Guanzhou Wei, Venkat Krishnan, Yu Xie, Manajit Sengupta, Yingchen Zhang, Haitao Liao, Xiao Liu

[The INFORMS Journal on Data Science @InformsJDataSci]

📝 Summary (2/2): This study explores integrating multisource remote-sensing data streams to infer accurate aerosol optical depth (AOD) measurements. The proposed statistical model addresses heterogeneous characteristics in the data streams and includes a bias correction process.

[The INFORMS Journal on Data Science @InformsJDataSci]

(2/2) this paper proposes a cost-sensitive adversarial data augmentation framework to make overparameterized models cost-sensitive. The overarching idea is to generate targeted adversarial examples that push the decision boundary in cost-aware directions.

[The INFORMS Journal on Data Science @InformsJDataSci]

🔔 New Article Alert at @InformsJDataSci
"Rethinking Cost-Sensitive Classification in Deep Learning via Adversarial Data Augmentation" by Qiyuan Chen, Raed Al Kontar, Maher Nouiehed, X. Jessie Yang, Corey Lester

🖇 Link to Article: https://t.co/rNCEdFGbcS

[The INFORMS Journal on Data Science @InformsJDataSci]

📝 Summary (1/2): Cost-sensitive classification is critical in applications where misclassification errors widely vary in cost. However, overparameterization poses fundamental challenges to the cost-sensitive modeling of deep neural networks (DNNs). To address this challenge, ...

[The INFORMS Journal on Data Science @InformsJDataSci]

📷 Summary: This work leverages a computational conjecturing framework to produce nonlinear bounds for continuous features and boolean expressions for categorical features based on input data. Their method recovers known patterns in data that no previous method could find.

[The INFORMS Journal on Data Science @InformsJDataSci]

(1/2) 📷New Article Alert in @InformsJDataSci

"Conjecturing-Based Discovery of Patterns in Data" by J. Paul Brooks, David J. Edwards, Craig E. Larson, and Nico Van Cleemput

📷 Link to Article: https://t.co/QZanbRIzZs

📷 Link to Presentation Video: https://t.co/c3Lbhqf5AF

[The INFORMS Journal on Data Science @InformsJDataSci]

We are thrilled to welcome our new associate editors Associate Professor Jing Wang and Professor Wenjun Zhou #informs #informs2024 @gshmueli

[The INFORMS Journal on Data Science @InformsJDataSci]

(3/3) This introduces high-dimensional analogs to the histogram. The Bayesian priors encourage sparsity, allowing for interpretability and the models are 50 times sparser than high-dimensional histograms on crime data that describe how often different types of break-ins occur.

[The INFORMS Journal on Data Science @InformsJDataSci]

(1/3)📷New Article Alert in @InformsJDataSci

"Sparse Density Trees and Lists: An Interpretable Alternative to High-Dimensional Histograms" by Siong Thye Goh, @lesiasemenova, @CynthiaRudin

🖇 Article: https://t.co/T9Zv7ZrQ1G
👩‍💻Code: https://t.co/tKa7uGIh4Z

[The INFORMS Journal on Data Science @InformsJDataSci]

(2/3) The authors introduce three tree-based density estimation methods for binary/categorical data. Its models are sparse, and users can specify the desired number of leaves, branches, or rules with a prior.

[The INFORMS Journal on Data Science @InformsJDataSci]

(3/3) "The hierarchical model adeptly captures intricate interactions, offering potential enhancements in decision-making, particularly in situations where enumerating all possible factor combinations is impractical, such as in financial risk scoring and pay structure design."

[The INFORMS Journal on Data Science @InformsJDataSci]

🎆 (1/3) Celebrate the arrival of New Year by delving into the article!
"Cost Patterns of Multiple Chronic Conditions: A Novel Modeling Approach Using a Condition Hierarchy" by Lida Apergi, Margret Bjarnadottir, John Baras, and Bruce L. Golden

🔗Link: https://t.co/xL9PX4C6MN

[The INFORMS Journal on Data Science @InformsJDataSci]

(2/3) "This work introduces a modeling approach, inspired by backward elimination and incorporating a cost hierarchy to minimize information loss. The cost of each condition is modeled as a function of the number of other, more expensive chronic conditions an individual has."