Informs Journal on Data Science - IJDS

01/07/2024

Welcome our new senior editor Associate Professor Eunshin Byon

28/06/2024

Editor-in-chief Galit Shmueli will be stepping down at the end of June 2024 from her EIC role. Dr. Shmueli is the inaugural EIC and has served in this role since 2020. Kwok Tsui was appointed as the interim EIC from July to December 2024. Dr. Tsui has served as IJDS Senior Editor since 2020. Manuscripts submitted prior to July 2024 will continue to be handled by Dr. Shmueli.

27/06/2024

🔥 New article alert in the Informs Journal on Data Science!

📝 Article Title: The Interplay Between Individual Mobility, Health Risk, and Economic Choice: A Holistic Model for COVID-19 Policy Intervention

👥 Authors (photos are ordered based on): Zihao Yang, Ramayya Krishnan, Beibei Li

🔗 DOI: https://doi.org/10.1287/ijds.2023.0013

🔍 Abstract: This paper addresses the dual policy objectives during the COVID pandemic: reducing mobility for public health and maintaining economic activity. The authors developed a data-informed approach to jointly model human mobility, health risk, and economic activity. Their method integrates epidemic models (e.g., SIR) with individual-level mobility data, creating a customizable public health risk assessment. Though developed for the pandemic, the model is generalizable for evaluating various policy interventions using proprietary data sets from both public and private sectors at individual and zip code levels.

07/06/2024

🔥 New article alert in the Informs Journal on Data Science - IJDS!

📝 Article 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://doi.org/10.1287/ijds.2023.0019

🔍 Abstract: 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. Comparative analysis highlights superior accuracy and flexibility compared to traditional methods. The model's dependable performance and non-parametric nature make it a robust forecasting tool suitable for integration into diverse decision support systems analyzing time series data.

21/05/2024

🔥 New article alert in Informs Journal on Data Science - IJDS

📝 Article Title: Multivariate Functional Clustering with Variable Selection and Application to Sensor Data from Engineering Systems

👥 Authors (photos are ordered based on): Zhongnan Jin, Jie Min, Yili Hong, Pang Du, Qingyu Yang

🔗 DOI: https://doi.org/10.1287/ijds.2022.0034

🔍 Abstract:

Multisensor data that track system operating behaviors are widely available in engineering systems. However, the possible presence of sensors whose data do not contain relevant information poses a challenge. The authors propose a functional data clustering method that simultaneously removes noninformative sensors and groups functional curves into clusters using informative sensors. Utilizing functional principal component analysis, data dimensionality is reduced. Gaussian mixture distribution aids in model-based clustering with variable selection, incorporating three penalty types (individual, variable, group).

22/04/2024

🔥 New article alert in the Informs Journal on Data Science - IJDS

📝 Article Title: "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 "YC" Zhang, Haitao Liao, Xiao Liu
🔗 DOI: https://doi.org/10.1287/ijds.2021.0058

🔍 Abstract:

Increasingly frequent wildfires have a significant impact on solar energy production as the atmospheric aerosols generated by wildfires diminish the incoming solar radiation. 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. Applied to California wildfires AOD data, the model demonstrates predictive capabilities and interoperability.

05/03/2024

💡New Article Alert in Informs Journal on Data Science - IJDS

"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://pubsonline.informs.org/doi/epdf/10.1287/ijds.2022.0033

📝 Summary: 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, this paper proposes a cost-sensitive adversarial data augmentation (CSADA) 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. These targeted adversarial samples are generated by maximizing the probability of critical misclassifications and used to train a model with more conservative decisions on costly pairs.

05/02/2024

💡New Article Alert in Informs Journal on Data Science - IJDS

"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://doi.org/10.1287/ijds.2021.0043
🔗 Link to Presentation Video: https://www.youtube.com/watch?v=DfqJZ4rtxbQ

📝 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.

We propose the use of a conjecturing machine that suggests feature relationships in the form of bounds involving nonlinear terms for numerical features and Boolean expressions for categorical featu...

29/01/2024

We are thrilled to welcome our new associate editors Associate Professor Jing Wang and Professor Wenjun Zhou

20/01/2024

💡New Article Alert in Informs Journal on Data Science - IJDS

"Sparse Density Trees and Lists: An Interpretable Alternative to High-Dimensional Histograms" by Siong Thye Goh, Lesia Semenova, Cynthia Rudin

🔗 Link to Article: https://doi.org/10.1287/ijds.2021.0001
🖥 Link to Code: https://codeocean.com/capsule/2414499/tree/v1

📝 Summary: The authors introduce three tree-based density estimation methods for categorical data. Its models are sparse, and users can specify the desired number of leaves, branches, or rules with a prior.

🙋‍♀️What is the most important finding in this work?
This work introduces high-dimensional analogs to the histogram. These are sparse piecewise constant density estimators for binary/categorical data. 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.

🔍 What is the impact of the research to the community?
The three methods produce sparse density estimation models that can be printed on an index card and yet provide insight into real datasets that could not have been reliably obtained in any other way. Visualizing the estimated density values can aid in understanding the data distribution and assist with decision-making.

02/01/2024

💡Celebrate the arrival of New Year 2024 by delving into the article published in Informs Journal on Data Science

"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 to Article: https://doi.org/10.1287/ijds.2022.0010

"This study introduces a unique modeling approach, drawing inspiration from 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. By applying this method to extensive claims data from 2007 to 2012, the research identifies individuals with one or more chronic conditions, estimates their total 2012 healthcare expenditures, and employs regression analysis and clustering to characterize the cost patterns of 69 chronic conditions. 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."

Healthcare cost predictions are widely used throughout the healthcare system. However, predicting these costs is complex because of both uncertainty and the complex interactions of multiple chronic...

23/12/2023

💡 Explore the new article at IJDS during your holiday break!

"Interpretable Hierarchical Deep Learning Model for Noninvasive Alzheimer's Disease Diagnosis" by Maryam Zokaeinikoo, Pooyan Kazemian, and Prasenjit Mitra

🔗 Link to Article: https://doi.org/10.1287/ijds.2020.0005
🖥️ Access the code at: https://codeocean.com/capsule/2881658/tree/v1

"This study introduces an interpretable hierarchical deep learning model for the noninvasive and affordable detection of Alzheimer's disease. The model utilizes transcripts of patient interviews, employing a novel hierarchical attention mechanism to capture temporal dependencies in longitudinal data. Results show a 96% accuracy in detecting Alzheimer's from interviews, offering interpretability through importance scores for words, sentences, and transcripts. This approach could enhance diagnosis, improve patient outcomes, and contribute to cost containment, providing a promising alternative to expensive and invasive imaging methods."

21/12/2023

💡 Explore the latest article at IJDS during your holiday break!

"Diversity Subsampling: Custom Subsamples from Large Data Sets" by Boyang Shang, Daniel W. Apley, and Sanjay Mehrotra

🔗 Link to Article: https://doi.org/10.1287/ijds.2022.00017

"This paper proposes a novel diversity subsampling algorithm suitable for large real-world data. It enjoys superior performance and is far faster than existing algorithms. It is more efficient and effective to select a diverse subsample from a large data set by requiring the subsample to approximate a uniform sample over the effective support of the data, relative to maximizing the minimum distance between points. A diverse subsample is beneficial in supervised learning settings to address covariate drift, find the global optimum of the response surface, etc."

🖥️ Access the code at: https://doi.org/10.24433/CO.8309237.v3

Subsampling from a large unlabeled (i.e., no response values are available yet) data set is useful in many supervised learning contexts to provide a global view of the data based on only a fraction...

04/12/2023

💡 Check out the new article at IJDS!

"Credit Risk Modeling with Graph Machine Learning" by Sanjiv Das, Xin Huang, Soji Adeshina, Patrick Yang, and Leonardo Bachega

Link to Article: https://doi.org/10.1287/ijds.2022.00018

"Credit ratings are traditionally generated from models that use financial statement data and market data, which are tabular (numeric and categorical). Using machine learning methods, this work constructs a network of firms using U.S. Securities and Exchange Commission (SEC) filings (denoted CorpNet) to enhance the traditional tabular data set with a corporate graph. This paper demonstrates that a corporate graph generated using text from SEC filings, used to fit graph neural networks (GNNs), performs better than traditional models based on tabular data alone. Constructing a network of corporate linkages is often challenging, and the paper shows how to do this with large-scale text processing. The community can use this approach to manufacture graphs for other applications as well. Additionally, this paper suggests that practitioners may want to use GNNs to improve existing credit rating models."

The code is available at https://codeocean.com/capsule/5230264/tree/v2

Accurate credit ratings are an essential ingredient in the decision-making process for investors, rating agencies, bond portfolio managers, bankers, and policy makers, as well as an important input...

28/11/2023

💡 Check out the new article at IJDS!

"Modeling Financial Products and Their Supply Chains" by Margrét Vilborg Bjarnadóttir and Louiqa Raschid

Link to Article: https://doi.org/10.1287/ijds.2020.0006

"The paper focuses on residential mortgage-backed securities, which were at the heart of the 2008 US financial crisis. We model and study how multiple financial institutions form a supply chain to create these securities. We show that communities of financial institutions along the supply chain are associated with the generation of a prospectus and a group of securities. We are the first to show that toxic communities that are closely linked to financial institutions that played a key role in the subprime crisis can increase the risk of failure of the securities."

The code is available at https://codeocean.com/capsule/7485173/tree/v1

The objective of this paper is to explore how novel financial datasets and machine learning methods can be applied to model and understand financial products. We focus on residential mortgage backe...

15/11/2023

Thrilled to share that our Editor-in-Chief, INFORMS Journal on Data Science Professor Galit Shmueli, has been honored as a 2023 INFORMS Information Systems Society (ISS) Distinguished Fellow. This prestigious award is a testament to her exceptional intellectual contributions in the information systems discipline.

Warmest congratulations to Professor Galit Shmueli for this well-deserved honor! 🌐✨ Link: https://lnkd.in/eWhTaVq4

28/10/2023

We are thrilled to welcome our new Associate Editors - Associate Professor Mochen Yang and Associate Professor Yongxiang Li

19/10/2023

Congratulations to the Journal of Data Science 2023 Best Associate Editor Awardees!