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1. Spatial Classification with Limited Observations Based on Physics-Aware Structural Constraint

   https://doi.org/10.1609/aaai.v34i01.5436  

   Sainju, Arpan Man; He, Wenchong; Jiang, Zhe; Yan, Da (June 2020, Proceedings of the AAAI Conference on Artificial Intelligence)

   Spatial classification with limited feature observations has been a challenging problem in machine learning. The problem exists in applications where only a subset of sensors are deployed at certain regions or partial responses are collected in field surveys. Existing research mostly focuses on addressing incomplete or missing data, e.g., data cleaning and imputation, classification models that allow for missing feature values, or modeling missing features as hidden variables and applying the EM algorithm. These methods, however, assume that incomplete feature observations only happen on a small subset of samples, and thus cannot solve problems where the vast majority of samples have missing feature observations. To address this issue, we propose a new approach that incorporates physics-aware structural constraints into the model representation. Our approach assumes that a spatial contextual feature is observed for all sample locations and establishes spatial structural constraint from the spatial contextual feature map. We design efficient algorithms for model parameter learning and class inference. Evaluations on real-world hydrological applications show that our approach significantly outperforms several baseline methods in classification accuracy, and the proposed solution is computationally efficient on a large data volume. 

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2. A METHOD TO GENERATE FLOOD MAPS IN 3D USING DEM AND DEEP LEARNING

   https://doi.org/10.5194/isprs-archives-XLIV-M-2-2020-25-2020  

   Gebrehiwot, A.; Hashemi-Beni, L. (January 2020, ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences)

   null (Ed.)

   Abstract. High-resolution remote sensing imagery has been increasingly used for flood applications. Different methods have been proposed for flood extent mapping from creating water index to image classification from high-resolution data. Among these methods, deep learning methods have shown promising results for flood extent extraction; however, these two-dimensional (2D) image classification methods cannot directly provide water level measurements. This paper presents an integrated approach to extract the flood extent in three-dimensional (3D) from UAV data by integrating 2D deep learning-based flood map and 3D cloud point extracted from a Structure from Motion (SFM) method. We fine-tuned a pretrained Visual Geometry Group 16 (VGG-16) based fully convolutional model to create a 2D inundation map. The 2D classified map was overlaid on the SfM-based 3D point cloud to create a 3D flood map. The floodwater depth was estimated by subtracting a pre-flood Digital Elevation Model (DEM) from the SfM-based DEM. The results show that the proposed method is efficient in creating a 3D flood extent map to support emergency response and recovery activates during a flood event. 

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3. Fast Multi-Modal Multi-Instance Support Vector Machine for Fine-grained Chest X-ray Recognition

   https://doi.org/10.1109/ICDM58522.2023.00164  

   Seo, Hoon; Wang, Hua (December 2023, IEEE ICDM 2023)

   Chest X-ray (CXR) analysis plays an important role in patient treatment. As such, a multitude of machine learning models have been applied to CXR datasets attempting automated analysis. However, each patient has a differing number of images per angle, and multi-modal learning should deal with the missing data for specific angles and times. Furthermore, the large dimensionality of multi-modal imaging data with the shapes inconsistent across the dataset introduces the challenges in training. In light of these issues, we propose the Fast Multi-Modal Support Vector Machine (FMMSVM) which incorporates modality-specific factorization to deal with missing CXRs in the specific angle. Our model is able to adjust the fine-grained details in feature extraction and we provide an efficient optimization algorithm scalable to a large number of features. In our experiments, FMMSVM shows clearly improved classification performance. 

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4. Online Hierarchical Multi-label Classification

   https://doi.org/10.1109/BigData59044.2023.10386110  

   Qi, Wenting; Chelmis, Charalampos (December 2023, IEEE International Conference on Big Data)

   Existing approaches for multi-label classification are trained offline, missing the opportunity to adapt to new data instances as they become available. To address this gap, an online multi-label classification method was proposed recently, to learn from data instances sequentially. In this work, we focus on multi-label classification tasks, in which the labels are organized in a hierarchy. We formulate online hierarchical multi-labeled classification as an online optimization task that jointly learns individual label predictors and a label threshold, and propose a novel hierarchy constraint to penalize predictions that are inconsistent with the label hierarchy structure. Experimental results on three benchmark datasets show that the proposed approach outperforms online multi-label classification methods, and achieves comparable to, or even better performance than offline hierarchical classification frameworks with respect to hierarchical evaluation metrics. 

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5. Spatio-Temporal Event Forecasting Using Incremental Multi-Source Feature Learning

   https://doi.org/10.1145/3464976  

   Zhao, Liang; Gao, Yuyang; Ye, Jieping; Chen, Feng; Ye, Yanfang; Lu, Chang-Tien; Ramakrishnan, Naren (April 2022, ACM Transactions on Knowledge Discovery from Data)

   The forecasting of significant societal events such as civil unrest and economic crisis is an interesting and challenging problem which requires both timeliness, precision, and comprehensiveness. Significant societal events are influenced and indicated jointly by multiple aspects of a society, including its economics, politics, and culture. Traditional forecasting methods based on a single data source find it hard to cover all these aspects comprehensively, thus limiting model performance. Multi-source event forecasting has proven promising but still suffers from several challenges, including (1) geographical hierarchies in multi-source data features, (2) hierarchical missing values, (3) characterization of structured feature sparsity, and (4) difficulty in model’s online update with incomplete multiple sources. This article proposes a novel feature learning model that concurrently addresses all the above challenges. Specifically, given multi-source data from different geographical levels, we design a new forecasting model by characterizing the lower-level features’ dependence on higher-level features. To handle the correlations amidst structured feature sets and deal with missing values among the coupled features, we propose a novel feature learning model based on an N th-order strong hierarchy and fused-overlapping group Lasso. An efficient algorithm is developed to optimize model parameters and ensure global optima. More importantly, to enable the model update in real time, the online learning algorithm is formulated and active set techniques are leveraged to resolve the crucial challenge when new patterns of missing features appear in real time. Extensive experiments on 10 datasets in different domains demonstrate the effectiveness and efficiency of the proposed models. 

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