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Enterprise data typically involves multiple heterogeneous data sources and external data that respectively record business activities, transactions, customer demographics, status, behaviors, interactions and communications with the enterprise, and the consumption and feedback of its products, services, production, marketing, operations, and management, etc. They involve enterprise DNA associated with domain-oriented transactions and master data, informational and operational metadata, and relevant external data. A critical challenge in enterprise data science is to enable an effective 'whole-of-enterprise' data understanding and data-driven discovery and decision-making on all-round enterprise DNA. Accordingly, here we introduce a neural encoder Table2Vec for automated universal representation learning of entities such as customers from all-round enterprise DNA with automated data characteristics analysis and data quality augmentation. The learned universal representations serve as representative and benchmarkable enterprise data genomes (similar to biological genomes and DNA in organisms) and can be used for enterprise-wide and domain-specific learning tasks. Table2Vec integrates automated universal representation learning on low-quality enterprise data and downstream learning tasks. Such automated universal enterprise representation and learning cannot be addressed by existing enterprise data warehouses (EDWs), business intelligence and corporate analytics systems, where 'enterprise big tables' are constructed with reporting and analytics conducted by specific analysts on respective domain subjects and goals. It addresses critical limitations and gaps of existing representation learning, enterprise analytics and cloud analytics, which are analytical subject, task and data-specific, creating analytical silos in an enterprise. We illustrate Table2Vec in characterizing all-round customer data DNA in an enterprise on complex heterogeneous multi-relational big tables to build universal customer vector representations. The learned universal representation of each customer is all-round, representative and benchmarkable to support both enterprise-wide and domain-specific learning goals and tasks in enterprise data science. Table2Vec significantly outperforms the existing shallow, boosting and deep learning methods typically used for enterprise analytics. We further discuss the research opportunities, directions and applications of automated universal enterprise representation and learning and the learned enterprise data DNA for automated, all-purpose, whole-of-enterprise and ethical machine learning and data science., (© 2021. The Author(s).)

Purpose: In this study, we developed radiomic models that utilize a combination of imaging features and clinical variables to distinguish endometrial cancer (EC) from normal endometrium on routine computed tomography (CT)., Methods: A total of 926 patients consisting of 416 endometrial cancer (EC) and 510 normal endometrium were included. The CT images of these patients were segmented manually, and divided into training, validation, testing and external testing sets. Non-texture and texture features of these images with endometrium or uterus as region of interest were extracted. The clinical feature "age" was also included in the feature set. Feature selection and machine learning classifier were applied to normalized feature set. This manual optimized combination was then compared with the best pipeline exported by Tree-Based Pipeline Optimization Tool (TPOT) on testing and external testing set. The performances of these machine learning pipelines were compared to that of radiologists., Results: The manual expert optimized pipeline using the "reliefF" feature selection method and "Bagging" classifier on the external testing set achieved a test ROC AUC of 0.73, accuracy of 0.73 (95% CI 0.62-0.82), sensitivity of 0.64 (95% CI 0.45-0.79), and specificity of 0.78 (95% CI 0.65-0.87), while TPOT achieved a test ROC AUC of 0.79, accuracy of 0.80 (95% CI 0.70-0.87), sensitivity of 0.61 (95% CI 0.43-0.77), and specificity of 0.90 (95% CI 0.78-0.96). When compared to average radiologist performance, the TPOT achieved higher test accuracy (0.80 vs. 0.49, p < 0.001) and specificity (0.90 vs. 0.51, p < 0.001), with comparable sensitivity (0.61 vs. 0.46, p = 0.130)., Conclusion: Our results demonstrate that automatic machine learning can distinguish EC from normal endometrium on routine CT imaging with higher accuracy and specificity than radiologists., (© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

The recently proposed learned bloom filter (LBF) opens a new perspective on how to reconstruct bloom filters with machine learning. However, the LBF has a massive time cost and does not apply to multidimensional spatial data. In this paper, we propose a prefix-based and adaptive learned bloom filter (PA-LBF) for spatial data, which efficiently supports the insertion and deletion. The proposed PA-LBF is divided into three parts: (1) the prefix-based classification. The Z-order space-filling curve is used to extract data, prefix it, and classify it. (2) The adaptive learning process. The multiple independent adaptive sub-LBFs are designed to train the suffixes of data, combined with part 1, to reduce the false positive rate (FPR), query, and learning process time consumption. (3) The backup filter uses CBF. Two kinds of backup CBF are constructed to meet the situation of different insertion and deletion frequencies. Experimental results prove the validity of the theory and show that the PA-LBF reduces the FPR by 84.87%, 79.53%, and 43.01% with the same memory usage compared with the LBF on three real-world spatial datasets. Moreover, the time consumption of PA-LBF can be reduced to 5 × and 2.05 × that of the LBF on the query and learning process, respectively. [ABSTRACT FROM AUTHOR]

With modern management of primary liver cancer shifting towards non-invasive diagnostics, accurate tumor classification on medical imaging is increasingly critical for disease surveillance and appropriate targeting of therapy. Recent advancements in machine learning raise the possibility of automated tools that can accelerate workflow, enhance performance, and increase the accessibility of artificial intelligence to clinical researchers. We explore the use of an automated Tree-Based Optimization Tool that leverages a genetic programming algorithm for differentiation of the two common primary liver cancers on multiphasic MRI. Manual and automated analyses were performed to select an optimal machine learning model, with an accuracy of 73–75% (95% CI 0.59–0.85), sensitivity of 70–75% (95% CI 0.48–0.89), and specificity of 71–79% (95% CI 0.52–0.90) on manual optimization, and an accuracy of 73–75% (95% CI 0.59–0.85), sensitivity of 65–75% (95% CI 0.43–0.89) and specificity of 75–79% (95% CI 0.56–0.90) for automated machine learning. We found that automated machine learning performance was similar to that of manual optimization, and it could classify hepatocellular carcinoma and intrahepatic cholangiocarcinoma with an sensitivity and specificity comparable to that of radiologists. However, automated machine learning performance was poor on a subset of scans that met LI-RADS criteria for LR-M. Exploration of additional feature selection and classifier methods with automated machine learning to improve performance on LR-M cases as well as prospective validation in the clinical setting are needed prior to implementation. [ABSTRACT FROM AUTHOR]

Learning from categorical data is a critical yet challenging task. Current research focuses on either leveraging the complex interaction between and within categorical values to generate a numerical representation, or designing a model that can tackle this types of data directly. However, both of these paradigms overlook the relation between the data characteristics and learning model hypothesis. In this paper, we propose a model-aware categorical data embedding framework that jointly reveals the intrinsic categorical data characteristics and optimizes the fitness of the representation for the follow-up learning model. An ELM-aware and a SVM-aware representation methods have been instantiated under this framework. Extensive experiments of classification with the embedded representation on 17 data sets demonstrate that the proposed framework can significantly improve the categorical data representation performance compared with state-of-the-art competitors. [ABSTRACT FROM AUTHOR]

Multi-instance learning (MIL) is one of promising paradigms in the supervised learning aiming to handle real world classification problems where a classification target contains several featured sections, e.g., an image typically contains several salient regions. In this paper, we propose a highly efficient learning method for MI classification based on hierarchical extreme learning machine (ELM), called MI-ELM. Specifically, a double-hidden layers feedforward network (DLFN) is designed to serve as the MI classifier. Then, the MI classification is formulated as an optimization problem. Moreover, the output weights of DLFN can be analytically determined by solving the aforementioned optimization problem. The merits of MI-ELM are as follows: (i) MI-ELM extends the single-layer ELM to be a hierarchical one that well fits for training DLFNs in MI classification. (ii) The input and hidden-layer parameters of DLFNs are assigned randomly rather than tuned iteratively, and the output weights of DLFNs can be determined analytically in one step. Therefore, MI-ELM significantly enhances the efficiency of the DLFN without notable loss of the classification accuracy. Experimental results over several real-world data sets demonstrate that the proposed MI-ELM method significantly outperforms existing kernel methods for MI classification in terms of the classification accuracy and the classification time. [ABSTRACT FROM AUTHOR]

The recently proposed Learned Bloom Filter (LBF) provides a new perspective on large-scale membership queries by using machine learning to replace the traditional bloom filter. However, reducing the false positive rate (FPR) of the learned model with small memory usage, and supporting deletion efficiently become the new issues. In this paper, we propose a novel Two-layer Partitioned and Deletable Deep Bloom Filter (PDDBF) for large-scale membership query, which can reduce the FPR with small memory usage and support deletion efficiently. The proposed PDDBF consists of three main parts: (1) Data partition. To improve the classification accuracy of the learned model, the K-means cluster with the elbow method is used for the data partition. (2) Deep Bloom Filter. To reduce the FPR, deep learning models are used to construct multiple independent learning mechanisms, which correspond to the clusters obtained by part1. (3) Partitioned backup filter. To support deletion under the premise of ensuring low FPR and reducing query time consumption, combine the perfect hash (PH) table and counting bloom filters (CBFs) on the basis of the partition bloom filter. Experiments show that the proposed PDDBF reduces the FPR 87.13% with the same memory usage compared with the state-of-the-art PLBF on real-world URLs data set. Moreover, the PDDBF reduces the FPR 99.68% with the same memory usage and reduces the query time consumption to 2.61x that of the PLBF after data deletion, respectively. • A novel PDDBF model is proposed for large-scale membership query. • A data partition part is devised to improve the classification accuracy of the learned model. • Multiple deep bloom filters are designed to reduce false positive rate. • The PH table and the partitioned CBFs are combined to support deletion efficiently. • The false positive rate of the model is low and can efficiently support data deletion. [ABSTRACT FROM AUTHOR]

This paper proposes a novel algorithm, termed random Fourier extreme learning machine with ℓ 2 , 1 -norm regularization, to improve the robustness and compactness of the widely used extreme learning machine. In specific, we firstly introduce the random Fourier mappings as activation functions to approximate the Gaussian kernel, with the aim to improve the extendibility of the powerful kernel ELM algorithms. We then adopt the ℓ 2 , 1 -norm to eliminate the potential irrelevant neurons, resulting in a more compact and discriminative hidden layer. After that, we propose an efficient algorithm with proved convergence to solve the resultant optimization problem. Extensive experiments have been conducted on 30 benchmark data sets to compare the proposed algorithm with six popular extreme learning algorithms. As observed, our algorithm outperforms the enumerated hidden layer reinforcement algorithms. In addition, it significantly improves the computational efficiency of Gaussian kernel extreme learning machine with comparable classification and regression performance in large scale learning scenarios. [ABSTRACT FROM AUTHOR]