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  • Articles  (2,565)
  • Springer  (2,565)
  • American Association for the Advancement of Science (AAAS)
  • Elsevier
  • Knowledge and Information Systems  (771)
  • 8762
  • Computer Science  (2,565)
  • Sociology
  • Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
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  • Articles  (2,565)
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  • Springer  (2,565)
  • American Association for the Advancement of Science (AAAS)
  • Elsevier
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  • Computer Science  (2,565)
  • Sociology
  • Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
  • 1
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    Precise temporal slot filling via truth finding with data-driven commonsense (2020)
    Springer
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    Publication Date: 2020-07-16
    Print ISSN: 0219-1377
    Electronic ISSN: 0219-3116
    Topics: Computer Science
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  • 2
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    Surface pattern-enhanced relation extraction with global constraints (2020)
    Springer
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    Publication Date: 2020-08-18
    Print ISSN: 0219-1377
    Electronic ISSN: 0219-3116
    Topics: Computer Science
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  • 3
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    Transfer learning for class imbalance problems with inadequate data (2015)
    Springer
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    Publication Date: 2015-08-25
    Description: A fundamental problem in data mining is to effectively build robust classifiers in the presence of skewed data distributions. Class imbalance classifiers are trained specifically for skewed distribution datasets. Existing methods assume an ample supply of training examples as a fundamental prerequisite for constructing an effective classifier. However, when sufficient data are not readily available, the development of a representative classification algorithm becomes even more difficult due to the unequal distribution between classes. We provide a unified framework that will potentially take advantage of auxiliary data using a transfer learning mechanism and simultaneously build a robust classifier to tackle this imbalance issue in the presence of few training samples in a particular target domain of interest. Transfer learning methods use auxiliary data to augment learning when training examples are not sufficient and in this paper we will develop a method that is optimized to simultaneously augment the training data and induce balance into skewed datasets. We propose a novel boosting-based instance transfer classifier with a label-dependent update mechanism that simultaneously compensates for class imbalance and incorporates samples from an auxiliary domain to improve classification. We provide theoretical and empirical validation of our method and apply to healthcare and text classification applications.
    Print ISSN: 0219-1377
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    Topics: Computer Science
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  • 4
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    A compression method of double-array structures using linear functions (2015)
    Springer
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    Publication Date: 2015-08-11
    Description: A trie is one of the data structures for keyword search algorithms and is utilized in natural language processing, reserved words search for compilers and so on. The double-array and LOUDS are efficient representation methods for the trie. The double-array provides fast traversal at time complexity of O (1), but the space usage of the double-array is larger than that of LOUDS. LOUDS is a succinct data structure with bit-string, and its space usage is extremely compact. However, its traversal speed is not so fast. This paper presents a new compression method of the double-array with keeping the retrieval speed. Our new method compresses the double-array by dividing the double-array into blocks and by using linear functions. Experimental results for varied keywords show that our new method reduced space usage of the double-array up to about 44 %, and the retrieval speed of the new method was 9–14 times faster than that of LOUDS. Moreover, the results show that the construction speed of the new method was faster than that of the conventional method for a large keyword set.
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    Topics: Computer Science
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  • 5
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    Local bilateral clustering for identifying research topics and groups from bibliographical data (2015)
    Springer
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    Publication Date: 2015-08-13
    Description: The structure of scientific collaboration networks provides insight on the relationships between people and disciplines. In this paper, we study a bipartite graph connecting authors to publications and extract from it clusters of authors and articles, interpreting the author clusters as research groups and the article clusters as research topics. Visualisations are proposed to ease the interpretation of such clusters in terms of discovering leaders, the activity level, and other semantic aspects. We discuss the process of obtaining and preprocessing the information from scientific publications, the formulation and implementation of the clustering algorithm, and the creation of the visualisations. Experiments on a test data set are presented, using an initial prototype implementation of the proposed modules.
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    Topics: Computer Science
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  • 6
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    Multi-view ensemble learning: an optimal feature set partitioning for high-dimensional data classification (2015)
    Springer
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    Publication Date: 2015-09-22
    Description: Multi-view ensemble learning has the potential to address issues related to the high dimensionality of data. It attempts to utilize all the relevant only discarding the irrelevant features. The view of a dataset is the sub-table of the training data with respect to a subset of the feature set. The problem of discarding the irrelevant features and obtaining subsets of the relevant features is useful for dimension reduction and dealing with the problem of having fewer training examples than even the reduced set of relevant features. A feature set partitioning resulting in the blocks of relevant features may not yield multiple-view-based classifiers with good classification performance. In this work the optimal feature set partition approach has been proposed. Further, the ensemble learning from views aims to maximize the performance of the classifier. The experiments study the performance of random feature set partitioning, attribute bagging, view generation using attribute clustering, view construction using genetic algorithm and OFSP proposed method. The blocks of relevant feature subsets are used to construct the multi-view classifier ensemble using K-nearest neighbor, Naïve Bayesian and support vector machine algorithm applied to sixteen high-dimensional data sets from UCI machine learning repository. The performance parameters considered for comparison are classification accuracy, disagreement among the classifiers, execution time and percentage reduction of attributes.
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  • 7
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    Robust time-series retrieval using probabilistic adaptive segmental alignment (2015)
    Springer
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    Publication Date: 2015-10-28
    Description: Traditional pairwise sequence alignment is based on matching individual samples from two sequences, under time monotonicity constraints. However, in many application settings, matching subsequences (segments) instead of individual samples may bring in additional robustness to noise or local non-causal perturbations. This paper presents an approach to segmental sequence alignment that jointly segments and aligns two sequences, generalizing the traditional per-sample alignment. To accomplish this task, we introduce a distance metric between segments based on average pairwise distances and then present a modified pair-HMM (PHMM) that incorporates the proposed distance metric to solve the joint segmentation and alignment task. We also propose a relaxation to our model that improves the computational efficiency of the generic segmental PHMM. Our results demonstrate that this new measure of sequence similarity can lead to improved classification performance, while being resilient to noise, on a variety of sequence retrieval problems, from EEG to motion sequence classification.
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  • 8
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    Leveraging path information to generate predictions for parallel business processes (2015)
    Springer
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    Publication Date: 2015-05-30
    Description: In semi-structured processes, the set of activities that need to be performed, their order and whether additional steps are required are determined by human judgment. There is a growing demand for operational support of such processes during runtime particularly in the form of predictions about the likelihood of future tasks. We address the problem of making predictions for a running instance of a semi-structured process that contains parallel execution paths where the execution path taken by a process instance influences its outcome. In particular, we consider five different models for how to represent an execution trace as a path attribute for training a prediction model. We provide a methodology to determine whether parallel paths are independent, and whether it is worthwhile to model execution paths as independent based on a comparison of the information gain obtained by dependent and independent path representations. We tested our methodology by simulating a marketing campaign as a business process model and selected decision trees as the prediction model. In the evaluation, we compare the complexity and prediction accuracy of a prediction model trained with five different models.
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    Topics: Computer Science
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  • 9
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    Centrality measures-based algorithm to visualize a maximal common induced subgraph in large communication networks (2015)
    Springer
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    Publication Date: 2015-05-30
    Description: Communication networks are ubiquitous, increasingly complex, and dynamic. Predicting and visualizing common patterns in such a huge graph data of communication network is an essential task to understand active patterns evolved in the network. In this work, the problem is to find an active pattern in a communication network which is modeled as detection of a maximal common induced subgraph (CIS). The state of the communication network is captured as a time series of graphs which has periodic snapshots of logical communications within the network. A new centrality measure is proposed to assess the variation in successive graphs and to identify the behavior of each node in the time series graph. It extents help in the process of selecting a suitable candidate vertex for maximality in each step of the proposed algorithm. This paper is a pioneer attempt in using centrality measures to detect a maximal CIS of the huge graph database, which gives promising effect in the resultant graph in terms of large number of vertices. The algorithm has polynomial time complexity, and the efficiency of the algorithm is demonstrated by a series of experiments with synthetic graph datasets of different orders. The performance of real-time datasets further ensured the competence of the proposed algorithm.
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    Topics: Computer Science
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  • 10
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    Role updating in information systems using model checking (2016)
    Springer
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    Publication Date: 2016-07-31
    Description: The role-based access control (RBAC) has significantly simplified the management of users and permissions in information systems. In dynamic environments, systems are constantly undergoing changes, and accordingly, the associated configurations need to be updated in order to reflect the systems’ security evolutions. However, such updating process is generally complicated as the resulting system state is expected to meet necessary constraints. This paper presents an approach for assisting administrators to make a desirable update, in light of changes in RBAC systems. We propose a formalization of the update approach, investigate its properties, and develop an updating algorithm based on model checking techniques. Our experimental results demonstrate the effectiveness of the proposed approach.
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    Topics: Computer Science
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