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  • 1
    Language: English
    In: PloS one, 2018, Vol.13(11), pp.e0208349
    Description: [This corrects the article DOI: 10.1371/journal.pone.0205499.].
    Keywords: Sciences (General);
    E-ISSN: 1932-6203
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  • 2
    In: Journal of the American Society for Information Science and Technology, June 2012, Vol.63(6), pp.1270-1277
    Description: This study evaluates the utility of a publication power approach () for assessing the quality of journals in the field of artificial intelligence. is compared with the homson‐euters Institute for cientific Information () 5‐year and 2‐year impact factors and with expert opinion. The ranking produced by the method under study is only partially correlated with citation‐based measures (), but exhibits close agreement with expert survey rankings. A simple average of and power rankings results in a new ranking that is highly correlated with the expert survey rankings. This evidence suggests that power ranking can contribute to evaluating artificial intelligence journals.
    Keywords: Bibliographic Records
    ISSN: 1532-2882
    E-ISSN: 1532-2890
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  • 3
    In: Journal of the American Society for Information Science and Technology, September 2013, Vol.64(9), pp.1951-1959
    Description: This article presents a new arsimonious iter‐ased easure for assessing the quality of academic papers. This new measure is parsimonious as it looks for the set of citing authors (citers) who have read a certain paper. The arsimonious iter‐ased easure aims to address potential distortion in the values of existing citer‐based measures. These distortions occur because of various factors, such as the practice of hyperauthorship. This new measure is empirically compared with existing measures, such as the number of citers and the number of citations in the field of artificial intelligence (). The results show that the new measure is highly correlated with those two measures. However, the new measure is more robust against citation manipulations and better differentiates between prominent and nonprominent researchers than the above‐mentioned measures.
    Keywords: Bibliometric Scatter ; Citation Indexes
    ISSN: 1532-2882
    E-ISSN: 1532-2890
    E-ISSN: 23301643
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  • 4
    In: PLoS ONE, 2018, Vol.13(10)
    Description: Single-cell RNA sequencing (scRNA-seq) is an emerging technology for profiling the gene expression of thousands of cells at the single cell resolution. Currently, the labeling of cells in an scRNA-seq dataset is performed by manually characterizing clusters of cells or by fluorescence-activated cell sorting (FACS). Both methods have inherent drawbacks: The first depends on the clustering algorithm used and the knowledge and arbitrary decisions of the annotator, and the second involves an experimental step in addition to the sequencing and cannot be incorporated into the higher throughput scRNA-seq methods. We therefore suggest a different approach for cell labeling, namely, classifying cells from scRNA-seq datasets by using a model transferred from different (previously labeled) datasets. This approach can complement existing methods, and–in some cases–even replace them. Such a transfer-learning framework requires selecting informative features and training a classifier. The specific implementation for the framework that we propose, designated ''CaSTLe–classification of single cells by transfer learning,'' is based on a robust feature engineering workflow and an XGBoost classification model built on these features. Evaluation of CaSTLe against two benchmark feature-selection and classification methods showed that it outperformed the benchmark methods in most cases and yielded satisfactory classification accuracy in a consistent manner. CaSTLe has the additional advantage of being parallelizable and well suited to large datasets. We showed that it was possible to classify cell types using transfer learning, even when the databases contained a very small number of genes, and our study thus indicates the potential applicability of this approach for analysis of scRNA-seq datasets.
    Keywords: Research Article ; Biology And Life Sciences ; Medicine And Health Sciences ; Biology And Life Sciences ; Biology And Life Sciences ; Biology And Life Sciences ; Medicine And Health Sciences ; Biology And Life Sciences ; Medicine And Health Sciences ; Computer And Information Sciences ; Engineering And Technology ; Biology And Life Sciences ; Research And Analysis Methods ; Biology And Life Sciences ; Computer And Information Sciences
    E-ISSN: 1932-6203
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  • 5
    Language: English
    In: Information Fusion, January 2016, Vol.27, pp.111-125
    Description: A decision tree is a predictive model that recursively partitions the covariate’s space into subspaces such that each subspace constitutes a basis for a different prediction function. Decision trees can be used for various learning tasks including classification, regression and survival analysis. Due to their unique benefits, decision trees have become one of the most powerful and popular approaches in data science. Decision forest aims to improve the predictive performance of a single decision tree by training multiple trees and combining their predictions. This paper provides an introduction to the subject by explaining how a decision forest can be created and when it is most valuable. In addition, we are reviewing some popular methods for generating the forest, fusion the individual trees’ outputs and thinning large decision forests.
    Keywords: Decision Tree ; Decision Forest ; Random Forest ; Classification Tree ; Mathematics
    ISSN: 1566-2535
    E-ISSN: 1872-6305
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  • 6
    In: Journal of the American Society for Information Science and Technology, December 2013, Vol.64(12), pp.2548-2563
    Description: Recommendation systems often compute fixed‐length lists of recommended items to users. Forcing the system to predict a fixed‐length list for each user may result in different confidence levels for the computed recommendations. Reporting the system's confidence in its predictions (the recommendation strength) can provide valuable information to users in making their decisions. In this article, we investigate several different displays of a system's confidence to users and conclude that some displays are easier to understand and are favored by most users. We continue to investigate the effect confidence has on users in terms of their perception of the recommendation quality and the user experience with the system. Our studies show that it is not easier for users to identify relevant items when confidence is displayed. Still, users appreciate the displays and trust them when the relevance of items is difficult to establish.
    Keywords: Collaborative Filtering ; Human Computer Interaction
    ISSN: 1532-2882
    E-ISSN: 1532-2890
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  • 7
    Language: English
    In: Expert Systems With Applications, Nov 15, 2014, Vol.41(16), p.7507(17)
    Description: To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.eswa.2014.06.015 Byline: Lior Rokach, Alon Schclar, Ehud Itach Abstract: acents New framework of multi-label ensemble classification algorithms is introduced. acents The set covering problem (SCP) is used to construct the labelsets. acents Constraints are incorporated in the construction of the labelsets. acents Theoretical and empirical analysis and comparison are provided. acents The proposed methods outperform RAKEL and other state-of-the-art algorithms.
    ISSN: 0957-4174
    Source: Cengage Learning, Inc.
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  • 8
    Article
    Article
    Inderscience Publishers
    Language: English
    In: Int. J. of Granular Computing, Rough Sets and Intelligent Systems, 2012, Vol 2 Issue 3, pp 196 - 210
    Description: Privacy-preserving data mining aims to prevent the exposure of sensitive information as a result of mining algorithms. This is commonly achieved by data anonymisation. One way to anonymise data is by adherence to the k-anonymity concept which requires that the probability to identify an individual by linking databases does not exceed 1/k. In this paper, we propose an algorithm which utilises rough set theory to achieve k-anonymity. The basic idea is to partition the original dataset into several disjoint reducts such that each one of them adheres to k-anonymity. We show that it is easier to make each reduct comply with k-anonymity if it does not contain all quasi-identifier attributes. Moreover, our procedure ensures that even if the attacker attempts to rejoin the reducts, the k-anonymity is still preserved. Unlike other algorithms that achieve k-anonymity, the proposed method requires no prior knowledge of the domain hierarchy taxonomy.
    Keywords: k-anonimity; rough set theory; reducts; privacy preservation; data mining; privacy protection; data anonymisation; data security.
    ISSN: 1757-2703
    ISSN: 17572703
    ISSN: 1757-2711
    ISSN: 17572711
    E-ISSN: 1757-2711
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  • 9
    Article
    Article
    Language: English
    In: Artificial Intelligence Review, 2010, Vol.33(1), pp.1-39
    Description: The idea of ensemble methodology is to build a predictive model by integrating multiple models. It is well-known that ensemble methods can be used for improving prediction performance. Researchers from various disciplines such as statistics and AI considered the use of ensemble methodology. This paper, review existing ensemble techniques and can be served as a tutorial for practitioners who are interested in building ensemble based systems.
    Keywords: Ensemble of classifiers ; Supervised learning ; Classification ; Boosting
    ISSN: 0269-2821
    E-ISSN: 1573-7462
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  • 10
    Language: English
    In: Journal of the American Society for Information Science and Technology, December 2011, Vol.62(12), pp.2456-2470
    Description: Accurately evaluating a researcher and the quality of his or her work is an important task when decision makers have to decide on such matters as promotions and awards. Publications and citations play a key role in this task, and many previous studies have proposed using measurements based on them for evaluating researchers. Machine learning techniques as a way of enhancing the evaluating process have been relatively unexplored. We propose using a machine learning approach for evaluating researchers. In particular, the proposed method combines the outputs of three learning techniques (logistics regression, decision trees, and artificial neural networks) to obtain a unified prediction with improved accuracy. We conducted several experiments to evaluate the model's ability to: (a) classify researchers in the field of artificial intelligence as Association for the Advancement of Artificial Intelligence (AAAI) fellows and (b) predict the next AAAI fellowship winners. We show that both our classification and prediction methods are more accurate than are previous measurement methods, and reach a precision rate of 96% and a recall of 92%.
    Keywords: Engineering ; Library & Information Science;
    ISSN: 1532-2882
    E-ISSN: 1532-2890
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