107 research outputs found

    Document Clustering

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    In a world flooded with information, document clustering is an important tool that can help categorize and extract insight from text collections. It works by grouping similar documents, while simultaneously discriminating between groups. In this article, we provide a brief overview of the principal techniques used to cluster documents, and introduce a series of novel deep-learning based methods recently designed for the document clustering task. In our overview, we point the reader to salient works that can provide a deeper understanding of the topics discussed

    Improving Risk Assessment of Miscarriage During Pregnancy with Knowledge Graph Embeddings

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    Miscarriages are the most common type of pregnancy loss, mostly occurring in the first 12 weeks of pregnancy. Pregnancy risk assessment aims to quantify evidence to reduce such maternal morbidities, and personalized decision support systems are the cornerstone of high-quality, patient-centered care to improve diagnosis, treatment selection, and risk assessment. However, data sparsity and the increasing number of patient-level observations require more effective forms of representing clinical knowledge to encode known information that enables performing inference and reasoning. Whereas knowledge embedding representation has been widely explored in the open domain data, there are few efforts for its application in the clinical domain. In this study, we contrast differences among multiple embedding strategies, and we demonstrate how these methods can assist in performing risk assessment of miscarriage before and during pregnancy. Our experiments show that simple knowledge embedding approaches that utilize domain-specific metadata perform better than complex embedding strategies, although both can improve results comparatively to a population probabilistic baseline in both AUPRC, F1-score, and a proposed normalized version of these evaluation metrics that better reflects accuracy for unbalanced datasets. Finally, embedding approaches provide evidence about each individual, supporting explainability for its model predictions in such a way that humans understand

    Reduced hyperBF networks : practical optimization, regularization, and applications in bioinformatics.

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    A hyper basis function network (HyperBF) is a generalized radial basis function network (RBF) where the activation function is a radial function of a weighted distance. The local weighting of the distance accounts for the variation in local scaling and discriminative power along each feature. Such generalization makes HyperBF networks capable of interpolating decision functions with high accuracy. However, such complexity makes HyperBF networks susceptible to overfitting. Moreover, training a HyperBF network demands weights, centers and local scaling factors to be optimized simultaneously. In the case of a relatively large dataset with a large network structure, such optimization becomes computationally challenging. In this work, a new regularization method that performs soft local dimension reduction and weight decay is presented. The regularized HyperBF (Reduced HyperBF) network is shown to provide classification accuracy comparable to a Support Vector Machines (SVM) while requiring a significantly smaller network structure. Furthermore, the soft local dimension reduction is shown to be informative for ranking features based on their localized discriminative power. In addition, a practical training approach for constructing HyperBF networks is presented. This approach uses hierarchal clustering to initialize neurons followed by a gradient optimization using a scaled Rprop algorithm with a localized partial backtracking step (iSRprop). Experimental results on a number of datasets show a faster and smoother convergence than the regular Rprop algorithm. The proposed Reduced HyperBF network is applied to two problems in bioinformatics. The first is the detection of transcription start sites (TSS) in human DNA. A novel method for improving the accuracy of TSS recognition for recently published methods is proposed. This method incorporates a new metric feature based on oligonucleotide positional frequencies. The second application is the accurate classification of microarray samples. A new feature selection algorithm based on a Reduced HyperBF network is proposed. The method is applied to two microarray datasets and is shown to select a minimal subset of features with high discriminative information. The algorithm is compared to two widely used methods and is shown to provide competitive results. In both applications, the final Reduced HyperBF network is used for higher level analysis. Significant neurons can indicate subpopulations, while local active features provide insight into the characteristics of the subpopulation in specific and the whole class in general

    Machine Learning for the New York City Power Grid

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    Power companies can benefit from the use of knowledge discovery methods and statistical machine learning for preventive maintenance. We introduce a general process for transforming historical electrical grid data into models that aim to predict the risk of failures for components and systems. These models can be used directly by power companies to assist with prioritization of maintenance and repair work. Specialized versions of this process are used to produce (1) feeder failure rankings, (2) cable, joint, terminator, and transformer rankings, (3) feeder Mean Time Between Failure (MTBF) estimates, and (4) manhole events vulnerability rankings. The process in its most general form can handle diverse, noisy, sources that are historical (static), semi-real-time, or real-time, incorporates state-of-the-art machine learning algorithms for prioritization (supervised ranking or MTBF), and includes an evaluation of results via cross-validation and blind test. Above and beyond the ranked lists and MTBF estimates are business management interfaces that allow the prediction capability to be integrated directly into corporate planning and decision support; such interfaces rely on several important properties of our general modeling approach: that machine learning features are meaningful to domain experts, that the processing of data is transparent, and that prediction results are accurate enough to support sound decision making. We discuss the challenges in working with historical electrical grid data that were not designed for predictive purposes. The “rawness” of these data contrasts with the accuracy of the statistical models that can be obtained from the process; these models are sufficiently accurate to assist in maintaining New York City's electrical grid

    Scalable Hardware Efficient Deep Spatio-Temporal Inference Networks

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    Deep machine learning (DML) is a promising field of research that has enjoyed much success in recent years. Two of the predominant deep learning architectures studied in the literature are Convolutional Neural Networks (CNNs) and Deep Belief Networks (DBNs). Both have been successfully applied to many standard benchmarks with a primary focus on machine vision and speech processing domains. Many real-world applications involve time-varying signals and, consequently, necessitate models that efficiently represent both temporal and spatial attributes. However, neither DBNs nor CNNs are designed to naturally capture temporal dependencies in observed data, often resulting in the inadequate transformation of spatio-temporal signals into wide spatial structures. It is argued that deep machine learning without proper temporal representation mechanisms is unable to extract meaningful information from many time-varying natural signals. Another clear emerging need is in growing deep learning architectures with the size of the problem at hand, suggesting that such architectures should map well to custom hardware platforms. The latter offer much better performance than that achievable using CPUs or even GPUs. Analog computation is a unique potential solution to the scalability challenge offering the benefits of low power consumption and smaller physical size when compared to digital implementations. However, these benefits come with the consequence of inaccurate computations and noise. This work presents an enhanced formulation of DeSTIN - a Deep Spatio-Temporal Inference Network (DeSTIN) that is inherently designed to capture both spatial and temporal dependencies in the data provided. The regular structure of DeSTIN, its computational requirements, and local connectivity render it hardware-efficient and highly scalable. Implementation of DeSTIN using analog computation is studied in detail, where the architectural robustness to various distortions in its signals is demonstrated. To the best of our knowledge, this is the first time custom analog hardware has been developed for deep machine learning. Key enhancements to previous formulations of DeSTIN are discussed in detail and results on standard benchmarks are presented. This work helps pave the way for advancing deep learning to address some of the long-standing challenges in machine learning

    Towards a parallel computationally efficient approach to scaling up data stream classification

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    Advances in hardware technologies allow to capture and process data in real-time and the resulting high throughput data streams require novel data mining approaches. The research area of Data Stream Mining (DSM) is developing data mining algorithms that allow us to analyse these continuous streams of data in real-time. The creation and real-time adaption of classification models from data streams is one of the most challenging DSM tasks. Current classifiers for streaming data address this problem by using incremental learning algorithms. However, even so these algorithms are fast, they are challenged by high velocity data streams, where data instances are incoming at a fast rate. This is problematic if the applications desire that there is no or only a very little delay between changes in the patterns of the stream and absorption of these patterns by the classifier. Problems of scalability to Big Data of traditional data mining algorithms for static (non streaming) datasets have been addressed through the development of parallel classifiers. However, there is very little work on the parallelisation of data stream classification techniques. In this paper we investigate K-Nearest Neighbours (KNN) as the basis for a real-time adaptive and parallel methodology for scalable data stream classification tasks

    Predicting Community Evolution in Social Networks

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    Nowadays, sustained development of different social media can be observed worldwide. One of the relevant research domains intensively explored recently is analysis of social communities existing in social media as well as prediction of their future evolution taking into account collected historical evolution chains. These evolution chains proposed in the paper contain group states in the previous time frames and its historical transitions that were identified using one out of two methods: Stable Group Changes Identification (SGCI) and Group Evolution Discovery (GED). Based on the observed evolution chains of various length, structural network features are extracted, validated and selected as well as used to learn classification models. The experimental studies were performed on three real datasets with different profile: DBLP, Facebook and Polish blogosphere. The process of group prediction was analysed with respect to different classifiers as well as various descriptive feature sets extracted from evolution chains of different length. The results revealed that, in general, the longer evolution chains the better predictive abilities of the classification models. However, chains of length 3 to 7 enabled the GED-based method to almost reach its maximum possible prediction quality. For SGCI, this value was at the level of 3 to 5 last periods.Comment: Entropy 2015, 17, 1-x manuscripts; doi:10.3390/e170x000x 46 page
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