Data annotation is an essential stage in supervised learning. However, the annotation process is exhaustive and time consuming, specially for large datasets. Activities of Daily Living (ADL) recognition is an example of systems that exploit very large raw sensor data readings. In such systems, sensor readings are collected from activity-monitoring sensors in a 24/7 manner. The size of the generated dataset is so huge that it is almost impossible for a human annotator to give a certain label to every single instance in the dataset. This results in annotation gaps in the input data to the adopting supervised learning system. The performance of the recognition system is negatively affected by these gaps. In this work, we propose and investigate three different paradigms to handle these gaps. In the first paradigm, the gaps are taken out by dropping all unlabeled readings. A single "Unknown" or "Do-Nothing" label is given to the unlabeled readings within the operation of the second paradigm. The last paradigm handles these gaps by giving every one of them a unique label identifying the encapsulating deterministic labels. Also, we propose a semantic preprocessing method of annotation gaps by constructing a hybrid combination of some of these paradigms for further performance improvement. The performance of the proposed three paradigms and their hybrid combination is evaluated using an ADL benchmark dataset containing more than $2.5\times 10^6$ sensor readings that had been collected over more than nine months. The evaluation results emphasize the performance contrast under the operation of each paradigm and support a specific gap handling approach for better performance.
University of California Los Angeles（加利福尼亚大学洛杉矶分校）
Decision trees are a popular family of models due to their attractive properties such as interpretability and ability to handle heterogeneous data. Concurrently, missing data is a prevalent occurrence that hinders performance of machine learning models. As such, handling missing data in decision trees is a well studied problem. In this paper, we tackle this problem by taking a probabilistic approach. At deployment time, we use tractable density estimators to compute the "expected prediction" of our models. At learning time, we fine-tune parameters of already learned trees by minimizing their "…
Multi-label classification has received considerable interest in recent years. Multi-label classifiers have to address many problems including: handling large-scale datasets with many instances and a large set of labels, compensating missing label assignments in the training set, considering correlations between labels, as well as exploiting unlabeled data to improve prediction performance. To tackle datasets with a large set of labels, embedding-based methods have been proposed which seek to represent the label assignments in a low-dimensional space. Many state-of-the-art embedding-based meth…
Università degli Studi di Perugia（佩鲁贾大学）；University College Dublin（都柏林大学学院）
Gaussian Mixture models (GMMs) are a powerful tool for clustering, classification and density estimation when clustering structures are embedded in the data. The presence of missing values can largely impact the GMMs estimation process, thus handling missing data turns out to be a crucial point in clustering, classification and density estimation. Several techniques have been developed to impute the missing values before model estimation. Among these, multiple imputation is a simple and useful general approach to handle missing data. In this paper we propose two different methods to fit Gaussi…
Machine learning with missing data has been approached in two different ways, including feature imputation where missing feature values are estimated based on observed values, and label prediction where downstream labels are learned directly from incomplete data. However, existing imputation models tend to have strong prior assumptions and cannot learn from downstream tasks, while models targeting label prediction often involve heuristics and can encounter scalability issues. Here we propose GRAPE, a graph-based framework for feature imputation as well as label prediction. GRAPE tackles the mi…
Fondazione Bruno Kessler Research Institute（布鲁诺·凯斯勒基金会研究所）；Humanitas Research Hospital（胡曼尼塔斯研究医院）
Blood lactate concentration is a strong indicator of mortality risk in critically ill patients. While frequent lactate measurements are necessary to assess patient's health state, the measurement is an invasive procedure that can increase risk of hospital-acquired infections. For this reason we formally define the problem of lactate prediction as a clinically relevant benchmark problem for machine learning community so as to assist clinical decision making in blood lactate testing. Accordingly, we demonstrate the relevant challenges of the problem and its data in addition to the adopted soluti…
Data mining and machine learning techniques such as classification and regression trees (CART) represent a promising alternative to conventional logistic regression for propensity score estimation. Whereas incomplete data preclude the fitting of a logistic regression on all subjects, CART is appealing in part because some implementations allow for incomplete records to be incorporated in the tree fitting and provide propensity score estimates for all subjects. Based on theoretical considerations, we argue that the automatic handling of missing data by CART may however not be appropriate. Using…
Alan Turing Institute（艾伦·图灵研究所）；University Carlos III in Madrid（马德里卡洛斯三世大学）；University of Cambridge（剑桥大学）；Uber AI Labs（优步人工智能实验室）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）
Variational autoencoders (VAEs), as well as other generative models, have been shown to be efficient and accurate for capturing the latent structure of vast amounts of complex high-dimensional data. However, existing VAEs can still not directly handle data that are heterogenous (mixed continuous and discrete) or incomplete (with missing data at random), which is indeed common in real-world applications. In this paper, we propose a general framework to design VAEs suitable for fitting incomplete heterogenous data. The proposed HI-VAE includes likelihood models for real-valued, positive real val…
Handling Missing Data in Downstream Tasks With Distribution-Preserving Guarantees
Institute of Computer Science University of Rostock（计算机科学研究所罗斯托克大学）；Institute of Computer Science / Soda University of Rostock / Inria Saclay（计算机科学研究所/ Soda罗斯托克大学/ Inria萨克雷）；Department of Data Science IISER Thiruvananthapuram（数据科学系IISER特里凡纳塔普拉姆）
针对分类等下游任务中的缺失数据问题，提出可与分类器联合训练的 F3I 迭代 K 近邻插补方法，并给出分布保持理论保证与实验验证。
Handling missing values in clinical machine learning: Insights from an expert study
Chalmers University of Technology（查尔姆斯理工大学）；Gothenburg University（哥德堡大学）；Sorbonne University（索邦大学）；AP-HP（巴黎公共医疗集团）；Pitié-Salpêtrière Hospital（皮蒂-萨尔皮特里医院）；Inria（法国国家信息与自动化研究所）；Inserm（法国国家健康与医学研究院）；CHU Grenoble Alpes（格勒诺布尔阿尔卑斯大学医院中心）
Pontificia Universidad Javeriana（哈维里亚那天主教大学）；KU Leuven（鲁汶大学）；imec（比利时微电子研究中心）
The availability of genomic data has grown exponentially in the last decade, mainly due to the development of new sequencing technologies. Based on the interactions between genes (and gene products) extracted from the increasing genomic data, numerous studies have focused on the identification of associations between genes and functions. While these studies have shown great promise, the problem of annotating genes with functions remains an open challenge. In this work, we present a method to detect missing annotations in hierarchical multi-label classification datasets. We propose a method tha…
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）；AI Lab, Tencent（腾讯AI实验室）
Graph neural networks (GNNs) are popular weapons for modeling relational data. Existing GNNs are not specified for attribute-incomplete graphs, making missing attribute imputation a burning issue. Until recently, many works notice that GNNs are coupled with spectral concentration, which means the spectrum obtained by GNNs concentrates on a local part in spectral domain, e.g., low-frequency due to oversmoothing issue. As a consequence, GNNs may be seriously flawed for reconstructing graph attributes as graph spectral concentration tends to cause a low imputation precision. In this work, we pres…
We consider the problem of recovering an expert's reward function with inverse reinforcement learning (IRL) when there are missing/incomplete state-action pairs or observations in the demonstrated trajectories. This issue of missing trajectory data or information occurs in many situations, e.g., GPS signals from vehicles moving on a road network are intermittent. In this paper, we propose a tractable approach to directly compute the log-likelihood of demonstrated trajectories with incomplete/missing data. Our algorithm is efficient in handling a large number of missing segments in the demonstr…
This paper focuses on a novel and challenging detection scenario: A majority of true objects/instances is unlabeled in the datasets, so these missing-labeled areas will be regarded as the background during training. Previous art on this problem has proposed to use soft sampling to re-weight the gradients of RoIs based on the overlaps with positive instances, while their method is mainly based on the two-stage detector (i.e. Faster RCNN) which is more robust and friendly for the missing label scenario. In this paper, we introduce a superior solution called Background Recalibration Loss (BRL) th…
Missing data are present in most real world problems and need careful handling to preserve the prediction accuracy and statistical consistency in the downstream analysis. As the gold standard of handling missing data, multiple imputation (MI) methods are proposed to account for the imputation uncertainty and provide proper statistical inference. In this work, we propose Multiple Imputation via Generative Adversarial Network (MI-GAN), a deep learning-based (in specific, a GAN-based) multiple imputation method, that can work under missing at random (MAR) mechanism with theoretical support. MI-GA…
National Remote Sensing Centre (ISRO), India（印度国家遥感中心（ISRO））
Decision making from data involves identifying a set of attributes that contribute to effective decision making through computational intelligence. The presence of missing values greatly influences the selection of right set of attributes and this renders degradation in classification accuracies of the classifiers. As missing values are quite common in data collection phase during field experiments or clinical trails appropriate handling would improve the classifier performance. In this paper we present a review of recently developed missing value imputation algorithms and compare their perfor…
Partial Annotation Learning for Biomedical Entity Recognition
University of Chinese Academy of Sciences（中国科学院大学）；National Science Library, Chinese Academy of Sciences（中国科学院国家科学图书馆）；Institute for Logic, Language and Computation (ILLC), University of Amsterdam（阿姆斯特丹大学逻辑、语言与计算研究所）
Missing value imputation is a challenging and well-researched topic in data mining. In this paper, we propose IFGAN, a missing value imputation algorithm based on Feature-specific Generative Adversarial Networks (GAN). Our idea is intuitive yet effective: a feature-specific generator is trained to impute missing values, while a discriminator is expected to distinguish the imputed values from observed ones. The proposed architecture is capable of handling different data types, data distributions, missing mechanisms, and missing rates. It also improves post-imputation analysis by preserving inte…
Toshiba Corporation（东芝公司）；The Institute of Statistical Mathematics（统计数学研究所）
Sparse regression such as the Lasso has achieved great success in handling high-dimensional data. However, one of the biggest practical problems is that high-dimensional data often contain large amounts of missing values. Convex Conditioned Lasso (CoCoLasso) has been proposed for dealing with high-dimensional data with missing values, but it performs poorly when there are many missing values, so that the high missing rate problem has not been resolved. In this paper, we propose a novel Lasso-type regression method for high-dimensional data with high missing rates. We effectively incorporate me…
An important preliminary step of optical character recognition systems is the detection of text rows. To address this task in the context of historical data with missing labels, we propose a self-paced learning algorithm capable of improving the row detection performance. We conjecture that pages with more ground-truth bounding boxes are less likely to have missing annotations. Based on this hypothesis, we sort the training examples in descending order with respect to the number of ground-truth bounding boxes, and organize them into k batches. Using our self-paced learning method, we train a r…
Department of Computing Science Simon Fraser University（西蒙弗雷泽大学计算机科学系）
Missing data is a significant problem impacting all domains. State-of-the-art framework for minimizing missing data bias is multiple imputation, for which the choice of an imputation model remains nontrivial. We propose a multiple imputation model based on overcomplete deep denoising autoencoders. Our proposed model is capable of handling different data types, missingness patterns, missingness proportions and distributions. Evaluation on several real life datasets show our proposed model significantly outperforms current state-of-the-art methods under varying conditions while simultaneously im…
Interpretable Machine Learning for Cognitive Aging: Handling Missing Data and Uncovering Social Determinant
University of Texas Rio Grande Valley（德克萨斯大学里奥格兰德河谷分校）；Shandong Mental Health Center（山东省精神卫生中心）；Georgia Institute of Technology（佐治亚理工学院）；University of Texas Austin（德克萨斯大学奥斯汀分校）；Kennesaw State University（肯尼索州立大学）
Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques
Centre for Quantitative Medicine, Duke-NUS Medical School（杜克-新加坡国立大学医学院定量医学中心）；Programme in Health Services and Systems Research, Duke-NUS Medical School（杜克-新加坡国立大学医学院卫生服务与系统研究项目）；Department of Emergency Medicine, Singapore General Hospital（新加坡中央医院急诊医学科）；Department of Neurosurgery, Erasmus MC University Medical Center（伊拉斯姆斯大学医学中心神经外科）；Department of Statistics and Data Science, National University of Singapore（新加坡国立大学统计与数据科学系）；Department of Biostatistics and Bioinformatics, Duke University（杜克大学生物统计与生物信息学系）；SingHealth AI Office, Singapore Health Services（新加坡保健服务集团人工智能办公室）；Institute of Data Science, National University of Singapore（新加坡国立大学数据科学研究所）
Medical datasets are particularly subject to attribute noise, that is, missing and erroneous values. Attribute noise is known to be largely detrimental to learning performances. To maximize future learning performances it is primordial to deal with attribute noise before any inference. We propose a simple autoencoder-based preprocessing method that can correct mixed-type tabular data corrupted by attribute noise. No other method currently exists to handle attribute noise in tabular data. We experimentally demonstrate that our method outperforms both state-of-the-art imputation methods and nois…
Standard methods for anomaly detection assume that all features are observed at both learning time and prediction time. Such methods cannot process data containing missing values. This paper studies five strategies for handling missing values in test queries: (a) mean imputation, (b) MAP imputation, (c) reduction (reduced-dimension anomaly detectors via feature bagging), (d) marginalization (for density estimators only), and (e) proportional distribution (for tree-based methods only). Our analysis suggests that MAP imputation and proportional distribution should give better results than mean i…
Systems are commonly monitored for health and security through collection and streaming of multivariate time series. Advances in time series forecasting due to adoption of multilayer recurrent neural network architectures make it possible to forecast in high-dimensional time series, and identify and classify novelties early, based on subtle changes in the trends. However, mainstream approaches to multi-variate time series predictions do not handle well cases when the ongoing forecast must include uncertainty, nor they are robust to missing data. We introduce a new architecture for time series …
UiT The Arctic University of Norway（挪威北极大学）；Universidad Rey Juan Carlos（胡安卡洛斯国王大学）
Similarity-based approaches represent a promising direction for time series analysis. However, many such methods rely on parameter tuning, and some have shortcomings if the time series are multivariate (MTS), due to dependencies between attributes, or the time series contain missing data. In this paper, we address these challenges within the powerful context of kernel methods by proposing the robust \emph{time series cluster kernel} (TCK). The approach taken leverages the missing data handling properties of Gaussian mixture models (GMM) augmented with informative prior distributions. An ensemb…
Segmentation uncertainty models predict a distribution over plausible segmentations for a given input, which they learn from the annotator variation in the training set. However, in practice these annotations can differ systematically in the way they are generated, for example through the use of different labeling tools. This results in datasets that contain both data variability and differing label styles. In this paper, we demonstrate that applying state-of-the-art segmentation uncertainty models on such datasets can lead to model bias caused by the different label styles. We present an upda…
University of Arkansas（阿肯色大学）；Brigham Young University（杨百翰大学）
Many data mining and data analysis techniques operate on dense matrices or complete tables of data. Real-world data sets, however, often contain unknown values. Even many classification algorithms that are designed to operate with missing values still exhibit deteriorated accuracy. One approach to handling missing values is to fill in (impute) the missing values. In this paper, we present a technique for unsupervised learning called Unsupervised Backpropagation (UBP), which trains a multi-layer perceptron to fit to the manifold sampled by a set of observed point-vectors. We evaluate UBP with t…
Many problems in machine learning and related application areas are fundamentally variants of conditional modeling and sampling across multi-aspect data, either multi-view, multi-modal, or simply multi-group. For example, sampling from the distribution of English sentences conditioned on a given French sentence or sampling audio waveforms conditioned on a given piece of text. Central to many of these problems is the issue of missing data: we can observe many English, French, or German sentences individually but only occasionally do we have data for a sentence pair. Motivated by these applicati…
UiT The Arctic University of Norway（挪威北极大学）；Universidad Rey Juan Carlos（胡安·卡洛斯国王大学）
Clinical measurements that can be represented as time series constitute an important fraction of the electronic health records and are often both uncertain and incomplete. Recurrent neural networks are a special class of neural networks that are particularly suitable to process time series data but, in their original formulation, cannot explicitly deal with missing data. In this paper, we explore imputation strategies for handling missing values in classifiers based on recurrent neural network (RNN) and apply a recently proposed recurrent architecture, the Gated Recurrent Unit with Decay, spec…
Nonparametric End-to-End Probabilistic Forecasting of Distributed Generation Outputs Considering Missing Data Imputation
Guangzhou Power Supply Bureau Guangdong Power Grid Co., Ltd.（广东电网有限责任公司广州供电局）；Tsinghua University（清华大学）；Tsinghua-Berkeley Shenzhen Institute (TBSI)（清华-伯克利深圳学院）
