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KDT-MOEA: A multiobjective optimization framework based on K-D trees

INFORMATION SCIENCES 2019年 503卷 200-218页

作者： Lacerda, Allysson S. M. Batista, Lucas S. Univ Fed Minas Gerais Operat Res & Complex Syst Lab Programa Posgrad Engn Eletr Ave Antonio Carlos 6627 BR-31270901 Belo Horizonte MG Brazil Univ Estadual Montes Claros Dept Ciencia Comp Ave Prof Rui Braga S-N BR-39401089 Montes Claros MG Brazil Univ Fed Minas Gerais Operat Res & Complex Syst Lab Dept Engn Eletr Ave Antonio Carlos 6627 BR-31270901 Belo Horizonte MG Brazil

This paper presents KDT-MOEA, a framework that takes advantage of a special kind of binary search tree, named K-D tree, to solve multiobjective optimization problems (MOPs). Our main goal is to explore the capabilities of this data structure to define neighborhood structures either in decision variables space or in objective space, as well as by switching between them at any time. The KDT-MOEA framework performance is compared with five state-of-the-art algorithms on the DTLZ, WFG and 1209 benchmarking problems with up to 15 objectives. Statistical tests demonstrate that KDT-MOEA was able to outperform the compared methods on most problems. In addition, in order to evaluate the flexibility and the potential of the proposed operators, extended versions of the compared algorithms are also presented. Empirical results pointed out that the new versions were superior to all five original MOEAs, indicating that the proposed operators can also be easily incorporated into existing MOEAs with different strategies to achieve better results. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Multiobjective optimization Evolutionary algorithms spatial data structures K-D trees Neighborhood operators

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Support Vector Machine for spatial Variation

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TRANSACTIONS IN GIS 2013年第1期17卷 41-61页

作者： Andris, Clio Cowen, David Wittenbach, Jason MIT Cambridge MA 02139 USA Univ S Carolina Columbia SC 29208 USA Penn State Univ University Pk PA 16802 USA

Large, multivariate geographic datasets have been used to characterize geographic space with the help of spatial data mining tools. In our study, we explore the sufficiency of the Support Vector Machine (SVM), a popular machine-learning technique for unsupervised classification and clustering, to help recognize hidden patterns in a college admissions dataset. Our college admissions dataset holds over 10,000 students applying to an undisclosed university during one undisclosed year. Students are qualified almost exclusively by their standardized test scores and school records, and a known admissions decision is rendered based on these criteria. Given that the university has a number of political, social and geographic econometric factors in its admissions decisions, we use SVM to find implicit spatial patterns that may favor students from certain geographic regions. We first explore the characteristics of the applicants in the college admissions case study. Next, we explain the SVM technique and our unique threshold line' methodology for both discrete (regional) and continuous (k-neighbors) space. We then analyze the results of the regional and k-neighbor tests in order to respond to the methodological and geographic research questions.

关键词： SUPPORT vector machines spatial variation MULTIVARIATE analysis spatial data structures data mining MACHINE learning

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A data Structure for Real-Time Aggregation Queries of Big Brain Networks

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NEUROINFORMATICS 2020年第1期18卷 131-149页

作者： Ganglberger, Florian Johann Kaczanowska, Joanna Haubensak, Wulf Buehler, Katja VRVis Res Ctr Vienna Austria Res Inst Mol Pathol Vienna Austria

Recent advances in neuro-imaging allowed big brain-initiatives and consortia to create vast resources of brain data that can be mined by researchers for their individual projects. Exploring the relationship between genes, brain circuitry, and behavior is one of the key elements of neuroscience research. This requires fusion of spatial connectivity data at varying scales, such as whole brain correlated gene expression, structural and functional connectivity. With ever-increasing resolution, these tend to exceed the past state-of-the art in size and complexity by several orders of magnitude. Since current analytical workflows in neuroscience involve time-consuming manual data-aggregation, incorporating efficient techniques for handling big connectivity data is a necessity. We propose a novel data structure enabling the interactive exploration of heterogeneous neurobiological connectivity data with billions of edges. Based on this data structure we realized Aggregation Queries, i.e. the aggregated connectivity from, to or between brain areas allows experts to compare the multimodal networks residing at different scales, or levels of hierarchically organized anatomical atlases. Executed on-demand on volumetric gene expression and connectivity data, they allow an interactive dissection of networks in real-time and based on their spatial context. The data structure is optimized in order to be accessible directly from the hard disk, since connectivity of large-scale networks typically exceeds the memory size of current consumer level PCs. This allows experts to embed and explore their own experimental data in the framework of public data resources without the need for their own large-scale infrastructure. Our data structure outperforms state-of-the-art graph engines in retrieving connectivity of arbitrary user defined local brain areas. We demonstrate the feasibility of our approach by analyzing fear-related functional neuroanatomy in mice. Further, we show its versatility

关键词： Brain networks spatial data structures Aggregation queries Structural connectivity Functional connectivity Large networks Hierarchical Parcellation Big data Interactive data mining

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Assessing the Veracity of Methods for Extracting Place Semantics from Flickr Tags

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TRANSACTIONS IN GIS 2013年第4期17卷 544-562页

作者： Mackaness, William A. Chaudhry, Omair Univ Edinburgh Edinburgh EH8 9XP Midlothian Scotland Publ Hlth England Salisbury Wilts England

The volume and potential value of user generated content (UGC) is ever growing. Multiply sourced, its value is greatly increased by the inclusion of metadata that adequately and accurately describes that content -particularly if such data are to be integrated with more formal data sets. Typically, digital photography is tagged with location and attribute information that variously describe the location, events or objects in the image. Often inconsistent and incomplete, these attributes reflect concepts at a range of geographic scales. From a spatial data integration perspective, the information relating to " place" is of primary interest. The challenge therefore is in selecting the most appropriate tags that best describe the geography of the image. This article presents a methodology based on an information retrieval technique that separates out " place related tags" from the remainder of the tags. Different scales of geography are identified by varying the size of the sampling area within which the imagery falls. This is applied in the context of urban environments, using Flickr imagery. Empirical analysis is then used to assess the correctness of the chosen tags (i. e. whether the tag correctly describes the geographic region in which the image was taken). Logistic regression and Bayesian inference are used to attach a probability value to each place tag. The high correlation values achieved indicate that this methodology can be used to automatically select place tags for any urban region and thus hierarchically structure UGC in order that it can be semantically integrated with other data sources.

关键词： TAGS (Metadata) DIGITAL photography spatial data structures data integration (Computer science) INFORMATION retrieval METHODOLOGY LOGISTIC regression analysis BAYESIAN analysis

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A greedy global search algorithm for connecting unstable periodic orbits with low energy cost.

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CELESTIAL MECHANICS & DYNAMICAL ASTRONOMY 2013年第2期117卷 201-213页

作者： Tsirogiannis, G. A. Markellos, V. V. Univ Patras Dept Engn Sci Patras Greece

A method for space mission trajectory design is presented in the form of a greedy global search algorithm. It uses invariant manifolds of unstable periodic orbits and its main advantage is that it performs a global search for the suitable legs of the invariant manifolds to be connected for a preliminary transfer design, as well as the appropriate points of the legs for maneuver application. The designed indirect algorithm bases the greedy choice on the optimality conditions that are assumed for the theoretical minimum transfer cost of a spacecraft when using invariant manifolds. The method is applied to a test case space mission design project in the Earth-Moon system and is found to compare favorably with previous techniques applied to the same project.

关键词： Space mission design Invariant manifolds Unstable periodic orbits Circular restricted three-body problem spatial data structures

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S(2)ProT: rank allocation by superpositioned propagation of topic relevance

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INTERNATIONAL JOURNAL OF WEB INFORMATION SYSTEMS 2008年第4期4卷 416-440页

作者： Agren, Ola Umea Univ Dept Comp Sci Umea Sweden

Purpose - The purpose of this paper is to assign topic-specific ratings to web pages. Design/methodology/approach - The paper uses power iteration to assign topic-specific rating values (called relevance) to web pages, creating a ranking or partial order among these pages for each topic. This approach depends on a set of pages that are initially assumed to be relevant for a specific topic;the spatial link structure of the web pages;and a net-specific decay factor designated xi. Findings - The paper finds that this approach exhibits desirable properties such as fast convergence, stability and yields relevant answer sets. The first property will be shown using theoretical proofs, while the others are evaluated through stability experiments and assessments of real world data in comparison with already established algorithms. Research limitations/implications - In the assessment, all pages that a web spider was able to find in the Nordic countries were used. It is also important to note that entities that use domains outside the Nordic countries (e.g..com ***) are not present in the paper's datasets even though they reside logically within one or more of the Nordic countries. This is quite a large dataset, but still small in comparison with the entire worldwide web. Moreover, the execution speed of some of the algorithms unfortunately prohibited the use of a large test dataset in the stability tests. Practical implications - It is not only possible, but also reasonable, to perform ranking of web pages without using Markov chain approaches. This means that the work of generating answer sets for complex questions could (at least in theory) be divided into smaller parts that are later summed up to give the final answer. Originality/value - This paper contributes to the research on internet search engines.

关键词： Worldwide web Information retrieval spatial data structures Search engines

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A model-based approach for analysis of spatial structure in genetic data

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NATURE GENETICS 2012年第6期44卷 725-U163页

作者： Yang, Wen-Yun Novembre, John Eskin, Eleazar Halperin, Eran Univ Calif Los Angeles Interdept Program Bioinformat Los Angeles CA 90095 USA Univ Calif Los Angeles Dept Comp Sci Los Angeles CA 90024 USA Univ Calif Los Angeles Dept Ecol & Evolutionary Biol Los Angeles CA USA Univ Calif Los Angeles Dept Human Genet Los Angeles CA USA Int Comp Sci Inst Berkeley CA 94704 USA Tel Aviv Univ Sch Comp Sci IL-69978 Tel Aviv Israel Tel Aviv Univ Dept Mol Microbiol & Biotechnol IL-69978 Tel Aviv Israel

Characterizing genetic diversity within and between populations has broad applications in studies of human disease and evolution. We propose a new approach, spatial ancestry analysis, for the modeling of genotypes in two-or three-dimensional space. In spatial ancestry analysis (SPA), we explicitly model the spatial distribution of each SNP by assigning an allele frequency as a continuous function in geographic space. We show that the explicit modeling of the allele frequency allows individuals to be localized on the map on the basis of their genetic information alone. We apply our SPA method to a European and a worldwide population genetic variation data set and identify SNPs showing large gradients in allele frequency, and we suggest these as candidate regions under selection. These regions include SNPs in the well-characterized LCT region, as well as at loci including FOXP2, OCA2 and LRP1B.

关键词： spatial variation spatial data structures GENE frequency GENETIC polymorphisms FAMILIAL diseases GENETIC disorders

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On the Monotonic Lagrangian Grid as Antecedent to the Neighborhood Grid

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JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 2020年 142卷 13-15页

作者： Uhlmann, Jeffrey Univ Missouri 201 EBW Columbia MO 65211 USA

We note that a recently described data structure, the Neighborhood Grid, is equivalent to a data structure developed in the mid-1980s called the Monotonic Lagrangian Grid (MLG). The MLG was originally developed to support high-performance molecular and fluid dynamics simulations on both supercomputer and vector processing architectures and still finds use in those and other areas. In this paper we emphasize that the rediscovery of the MLG offers benefits to users of the Neighborhood Grid in the form of an existing literature with results relevant to its efficient implementation in various contexts while users of the MLG similarly benefit from new theoretical results obtained for the Neighborhood grid. (C) 2020 Elsevier Inc. All rights reserved.

关键词： MLG Monotonic Lagrangian grid Near-neighbor algorithms Neighborhood Grid spatial data structures

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Local histogram and discriminative learning-based hyperspectral data classification

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REMOTE SENSING LETTERS 2017年第1期8卷 86-95页

作者： Imani, Maryam Ghassemian, Hassan Tarbiat Modares Univ Fac Elect & Comp Engn Tehran Iran

A spectral-spatial hyperspectral image classification method is proposed in this article. The proposed method is local histogram and discriminative based classification (LHD). It is implemented in two steps. In the first step, a projection matrix is obtained by maximizing the class discrimination and preserving the local structure of data using both the spectral and spatial neighbours. In the second step, the contextual features are extracted from the local regions using the histogram as a nonparametric statistical estimate. Finally, with incorporating spectral and spatial features, the classification map is obtained. The experimental results demonstrate that the proposed method is superior to some state-of-the-art alternatives.

关键词： HYPERSPECTRAL imaging systems CLASSIFICATION METHODOLOGY SPECTRAL imaging spatial data structures HISTOGRAMS

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DYNAMIC ALGORITHMS IN COMPUTATIONAL GEOMETRY

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PROCEEDINGS OF THE IEEE 1992年第9期80卷 1412-1434页

作者： CHIANG, YJ TAMASSIA, R Dept. of Comput. Sci. Brown Univ. Providence RI

Research on dynamic algorithms for geometric problems has received increasing attention in recent years, and is motivated by many important applications in circuit layout, computer graphics, and computer-aided design. In this paper we survey dynamic algorithms and data structures in the area of computational geometry. Our work has a twofold purpose: it introduces the area to the nonspecialist and reviews the state of the art for the specialist.

关键词： balanced search trees computational geometry computational geometry convex hull data structures dynamic algorithms intersections point location proximity spatial data structures

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