A Statistically-Guided Deep Network Transformation and Moderation Framework for Data with Spatial Heterogeneity

Xie, Yiqun; He, Erhu; Jia, Xiaowei; Bao, Haikun; Zhou, Xun; Ghosh, Rahul; Ravirathinam, Praveen

doi:10.1109/icdm51629.2021.00088

Cited by 23 publications

(15 citation statements)

References 17 publications

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“…Due to the increased resemblance between neighboring data samples, spatial autocorrelation violates the independence principle. On the other hand, because the data generating processes frequently change with respect to space, spatial heterogeneity violates the identical distribution assumption [ 109 ]. To overcome spatial autocorrelation and spatial heterogeneity issues, the process of choosing flood and non-flood points was done iteratively and the set of points which the p -value of Moran’s I for all factors was obtained extremely close to zero and less than the threshold (i.e., 0.05) was chosen to create the flood inventory map.…”

Section: Discussionmentioning

confidence: 99%

Spatial Prediction of Current and Future Flood Susceptibility: Examining the Implications of Changing Climates on Flood Susceptibility Using Machine Learning Models

Gharakhanlou

Pérez

2022

Entropy

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The main aim of this study was to predict current and future flood susceptibility under three climate change scenarios of RCP2.6 (i.e., optimistic), RCP4.5 (i.e., business as usual), and RCP8.5 (i.e., pessimistic) employing four machine learning models, including Gradient Boosting Machine (GBM), Random Forest (RF), Multilayer Perceptron Neural Network (MLP-NN), and Naïve Bayes (NB). The study was conducted for two watersheds in Canada, namely Lower Nicola River, BC and Loup, QC. Three statistical metrics were used to validate the models: Receiver Operating Characteristic Curve, Figure of Merit, and F1-score. Findings indicated that the RF model had the highest accuracy in providing the flood susceptibility maps (FSMs). Moreover, the provided FSMs indicated that flooding is more likely to occur in the Lower Nicola River watershed than the Loup watershed. Following the RCP4.5 scenario, the area percentages of the flood susceptibility classes in the Loup watershed in 2050 and 2080 have changed by the following percentages from the year 2020 and 2050, respectively: Very Low = −1.68%, Low = −5.82%, Moderate = +6.19%, High = +0.71%, and Very High = +0.6% and Very Low = −1.61%, Low = +2.98%, Moderate = −3.49%, High = +1.29%, and Very High = +0.83%. Likewise, in the Lower Nicola River watershed, the changes between the years 2020 and 2050 and between the years 2050 and 2080 were: Very Low = −0.38%, Low = −0.81%, Moderate = −0.95%, High = +1.72%, and Very High = +0.42% and Very Low = −1.31%, Low = −1.35%, Moderate = −1.81%, High = +2.37%, and Very High = +2.1%, respectively. The impact of climate changes on future flood-prone places revealed that the regions designated as highly and very highly susceptible to flooding, grow in the forecasts for both watersheds. The main contribution of this study lies in the novel insights it provides concerning the flood susceptibility of watersheds in British Columbia and Quebec over time and under various climate change scenarios.

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Section: Discussionmentioning

confidence: 99%

Spatial Prediction of Current and Future Flood Susceptibility: Examining the Implications of Changing Climates on Flood Susceptibility Using Machine Learning Models

Gharakhanlou

Pérez

2022

Entropy

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“…S * can be efficiently solved using the Linear-Time Subset Scanning (LTSS) property [Neill, 2012;Xie et al, 2021c] combined with coordinate ascent. More solution details and results on regression are available in [Xie et al, 2021a]. -Active significance testing with learning: Once the optimal S * is identified, the current node H i j will be temporarily split into two children H i+1 j1 and H i+1 j2 , where one child corresponds to S * and the other for the rest of the space in H i j .…”

Section: Statistically-guided Transformationmentioning

confidence: 99%

“…Specifically, we carry out two training scenarios with and without the split (Fig. 2) and perform an upper-tailed dependent T-test on the losses from the two scenarios [Xie et al, 2021a]. For the split scenario a new network branch will be created (e.g., adding a copy of the last L layers to the network) to allow private parameters (Fig.…”

Section: Statistically-guided Transformationmentioning

confidence: 99%

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Statistically-Guided Deep Network Transformation to Harness Heterogeneity in Space (Extended Abstract)

Amanatidis

Birmpas

Filos-Ratsikas

et al. 2022

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence

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Allocating resources to individuals in a fair manner has been a topic of interest since the ancient times, with most of the early rigorous mathematical work on the problem focusing on infinitely divisible resources. Recently, there has been a surge of papers studying computational questions regarding various different notions of fairness for the indivisible case, like maximin share fairness (MMS) and envy-freeness up to any good (EFX). We survey the most important results in the discrete fair division literature, focusing on the case of additive valuation functions and paying particular attention to the progress made in the last 10 years.

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“…Previously, we developed a spatial-variability-aware neural network (SVANN) [5,6] that uses locationdependent weights and found that it could better model variability than one-size-fits-all (OSFA) approaches. Other methods [7,8] learn space partitions of the heterogeneous data and develop models tailored to the resulting homogeneous regions. However, these approaches either require dense training data for each spatial domain or struggle when significant variability exists within a domain.…”

Section: Introductionmentioning

confidence: 99%

SAMCNet: Towards a Spatially Explainable AI Approach for Classifying MxIF Oncology Data

Farhadloo

Molnar

Luo

et al. 2022

Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

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Given multi-category point sets from different place-types, our goal is to develop a spatially-lucid classifier that can distinguish between two classes based on the arrangements of their points. This problem is important for many applications, such as oncology, for analyzing immune-tumor relationships and designing new immunotherapies. It is challenging due to spatial variability and interpretability needs. Previously proposed techniques require dense training data or have limited ability to handle significant spatial variability within a single place-type. Most importantly, these deep neural network (DNN) approaches are not designed to work in non-Euclidean space, particularly point sets. Existing non-Euclidean DNN methods are limited to one-size-fitsall approaches. We explore a spatial ensemble framework that explicitly uses different training strategies, including weighted-distance learning rate and spatial domain adaptation, on various place-types for spatially-lucid classification. Experimental results on real-world datasets (e.g., MxIF oncology data) show that the proposed framework provides higher prediction accuracy than baseline methods.

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A Statistically-Guided Deep Network Transformation and Moderation Framework for Data with Spatial Heterogeneity

Cited by 23 publications

References 17 publications

Spatial Prediction of Current and Future Flood Susceptibility: Examining the Implications of Changing Climates on Flood Susceptibility Using Machine Learning Models

Spatial Prediction of Current and Future Flood Susceptibility: Examining the Implications of Changing Climates on Flood Susceptibility Using Machine Learning Models

Statistically-Guided Deep Network Transformation to Harness Heterogeneity in Space (Extended Abstract)

SAMCNet: Towards a Spatially Explainable AI Approach for Classifying MxIF Oncology Data

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