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ON THE nearest-neighbor algorithm FOR THE MEAN-FIELD TRAVELING SALESMAN PROBLEM

JOURNAL OF APPLIED PROBABILITY 2014年第1期51卷 106-117页

作者： Bandyopadhyay, Antar Sajadi, Farkhondeh Indian Stat Inst Delhi Ctr Theoret Stat & Math Unit New Delhi 110016 India

In this work we consider the mean-field traveling salesman problem, where the intercity distances are taken to be independent and identically distributed with some distribution F. We consider the simplest approximation algorithm, namely, the nearest-neighbor algorithm, where the rule is to move to the nearest nonvisited city. We show that the limiting behavior of the total length of the nearest-neighbor tour depends on the scaling properties of the density of F at 0 and derive the limits for all possible cases of general F.

关键词： nearest-neighbor algorithm mean-field setup traveling salesman problem

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PREDICTION OF PROTEIN SECONDARY STRUCTURE BY COMBINING nearest-neighbor algorithmS AND MULTIPLE SEQUENCE ALIGNMENTS

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JOURNAL OF MOLECULAR BIOLOGY 1995年第1期247卷 11-15页

作者： SALAMOV, AA SOLOVYEV, VV BAYLOR COLL MED DEPT CELL BIOLHOUSTONTX 77030

Recently Yi & Lander used a neural network and nearest-neighbor method with a scoring system that combined a sequence-similarity matrix with the local structural environment scoring scheme described by Bowie and co-workers for predicting protein secondary structure. We have improved their scoring system by taking into consideration N and C-terminal positions of alpha-helices and beta-strands and also beta-turns as distinctive types of secondary structure. Another improvement, which also decreases the time of computation, is performed by restricting a data base with a smaller subset of proteins that are similar with a query sequence. Using multiple sequence alignments rather than single sequences and a simple jury decision procedure our method reaches a sustained overall three-state accuracy of 72.2%, which is better than that observed for the most accurate multilayered neural-network approach, tested on the same data set of 126 non-homologous protein chains.

关键词： PROTEIN SECONDARY STRUCTURE PREDICTION nearest-neighbor algorithm LOCAL STRUCTURAL ENVIRONMENT SCORING MULTIPLE SEQUENCE ALIGNMENT EVOLUTIONARY INFORMATION

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A Hybrid Instance Selection Using nearest-neighbor for Cross-Project Defect Prediction

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Journal of Computer Science & Technology 2015年第5期30卷 969-980页

作者： Duksan Ryu Jong-In Jang Jongmoon Baik School of Computinq Korea Advanced Institute of Science and Technology Yuseong-gu Daejeon 305-701 Korea

Software defect prediction （SDP） is an active research field in software engineering to identify defect-prone modules. Thanks to SDP, limited testing resources can be effectively allocated to defect-prone modules. Although SDP requires sufficient local data within a company, there are cases where local data are not available, e.g., pilot projects. Companies without local data can employ cross-project defect prediction （CPDP） using external data to build classifiers. The major challenge of CPDP is different distributions between training and test data. To tackle this, instances of source data similar to target data are selected to build classifiers. Software datasets have a class imbalance problem meaning the ratio of defective class to clean class is far low. It usually lowers the performance of classifiers. We propose a Hybrid Instance Selection Using nearest-neighbor （HISNN） method that performs a hybrid classification selectively learning local knowledge （via k-nearest neighbor） and global knowledge （via na/ve Bayes）. Instances having strong local knowledge are identified via nearest-neighbors with the same class label. Previous studies showed low PD （probability of detection） or high PF （probability of false alarm） which is impractical to overall performance as well as high PD and low PF. use. The experimental results show that HISNN produces high overall performance as well as high PD and low PF.

关键词： software defect analysis instance-based learning nearest-neighbor algorithm data cleaning

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Incremental learning with sample queries

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 1998年第8期20卷 883-888页

作者： Ratsaby, J Manna Network Technol IL-61574 Tel Aviv Israel

The classical theory of pattern recognition assumes labeled examples appear according to unknown underlying class conditional probability distributions where the pattern classes are picked randomly in a passive manner according to their a priori probabilities. This paper presents experimental results for an incremental nearest-neighbor learning algorithm which actively selects samples from different pattern classes according to a querying rule as opposed to the a priori probabilities. The amount of improvement of this query-based approach over the passive batch approach depends on the complexity of the Bayes rule.

关键词： incremental learning sample querying nearest-neighbor algorithm active learning model selection

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Crustal structures beneath the Northern Jiangsu Basin and its surrounding areas: implications for geothermal prospecting

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JOURNAL OF GEOPHYSICS AND ENGINEERING 2022年第3期19卷 316-325页

作者： He, Kai Huang, Rong Xu, Yixian Hu, Shaoqian Wei, Penglong China Earthquake Adm Key Lab Earthquake Geodesy Inst Seismol Wuhan 430071 Peoples R China China Univ Geosci Inst Geophys & Geomat Hubei Subsurface Multiscale Imaging Key Lab Wuhan 430074 Peoples R China Zhejiang Univ Sch Earth Sci Hangzhou 310007 Peoples R China

Based on the recordings of 42 permanent seismic stations in Northern Jiangsu Basin (NJB) and its surrounding areas, we obtained both the sediment and crustal thickness in the region, using the nearest-neighbor algorithm and H-kappa stacking method of receiver functions. We proposed a solution to use the sediment thickness to correct the depth of actual Moho discontinuity beneath the basin areas. Our results indicate that the sediment thickness in the northwest of the NJB is <1 km. We suggest that the southeastern border of the NJB should extend to the Yangzhou-Rudong area. The results of Moho variations show that the undulations of the Moho beneath the western NJB retains the traces of the sinistral shearing movement of the Tan-Lu Fault since the Miocene. Finally, we suggest the area around the Gaoyou city to be a prospective area for geothermal exploration.

关键词： Northern Jiangsu Basin receiver function nearest-neighbor algorithm H-kappa stacking geothermal prospecting

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AIRCRAFT IDENTIFICATION USING A BILINEAR SURFACE REPRESENTATION OF RADAR DATA

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PATTERN RECOGNITION 1974年第1期6卷 35-&页

作者： WHITE, LJ KSIENSKI, AA OHIO STATE UNIV DEPT COMP & INFORMATION SCICOLUMBUSOH 43210 OHIO STATE UNIV DEPT ELECT ENGNELECTROSCI LABCOLUMBUSOH 43210

Low frequency radar scattering data is used for the identification of aircraft. It is shown that such radar data lies on two-dimensional surfaces in n-space. A bilinear approximation for these surfaces is described. Surface intersections using this approximation can be found simply and directly without solving a system of n simultaneous nonlinear equations. This intersection information can be used to show separability and effect feature reduction. The approximation is utilized to construct a modified nearest neighbor algorithm, which is evaluated by computer simulation experiments. These experiments showed a phenomenon of “bias”, where one aircraft data surface is more susceptible to misclassification in the presence of noise than the surface corresponding to another aircraft. This “bias” observed is shown to be related to the surface characteristics of the data surfaces involved, specifically proximity and relative curvature of corresponding points on the two surfaces.

关键词： Aircraft identification Radar data analysis Data surface structure Proximity intersection studies nearest-neighbor algorithm Separable data Probability of misclassification

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A comparison between different prediction models for invasive breast cancer occurrence in the French E3N cohort

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BREAST CANCER RESEARCH AND TREATMENT 2015年第2期150卷 415-426页

作者： Dartois, Laureen Gauthier, Emilien Heitzmann, Julia Baglietto, Laura Michiels, Stefan Mesrine, Sylvie Boutron-Ruault, Marie-Christine Delaloge, Suzette Ragusa, Stephane Clavel-Chapelon, Francoise Fagherazzi, Guy Ctr Res Epidemiol & Populat Hlth CESP INSERM U1018 Team 9 F-94805 Villejuif France Univ Paris 11 UMRS 1018 F-94805 Villejuif France Gustave Roussy F-94805 Villejuif France Statlife F-94805 Villejuif France Canc Council Victoria Canc Epidemiol Ctr Melbourne Vic Australia Univ Melbourne Ctr Mol Environm Genet & Analyt Epidemiol Sch Populat Hlth Melbourne Vic Australia Gustave Roussy Serv Biostat & Epidemiol F-94805 Villejuif France Gustave Roussy Dept Med Oncol F-94805 Villejuif France

Breast cancer remains a global health concern with a lack of high discriminating prediction models. The k-nearest-neighbor algorithm (kNN) estimates individual risks using an intuitive tool. This study compares the performances of this approach with the Cox and the Gail models for the 5-year breast cancer risk prediction. The study included 64,995 women from the French E3N prospective cohort. The sample was divided into a learning (N = 51,821) series to learn the models using fivefold cross-validation and a validation (N = 13,174) series to evaluate them. The area under the receiver operating characteristic curve (AUC) and the expected over observed number of cases (E/O) ratio were estimated. In the two series, 393 and 78 premenopausal and 537 and 98 postmenopausal breast cancers were diagnosed. The discrimination values of the best combinations of predictors obtained from cross-validation ranged from 0.59 to 0.60. In the validation series, the AUC values in premenopausal and postmenopausal women were 0.583 [0.520;0.646] and 0.621 [0.563;0.679] using the kNN and 0.565 [0.500;0.631] and 0.617 [0.561;0.673] using the Cox model. The E/O ratios were 1.26 and 1.28 in premenopausal women and 1.44 and 1.40 in postmenopausal women. The applied Gail model provided AUC values of 0.614 [0.554;0.675] and 0.549 [0.495;0.604] and E/O ratios of 0.78 and 1.12. This study shows that the prediction performances differed according to menopausal status when using parametric statistical tools. The k-nearest-neighbor approach performed well, and discrimination was improved in postmenopausal women compared with the Gail model.

关键词： Breast cancer Women Risk score Proportional hazard Cox regression nearest-neighbor algorithm Discrimination Calibration Postmenopausal women Premenopausal women Menopausal status Gail model

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PROTEIN SECONDARY STRUCTURE PREDICTION USING nearest-neighbor METHODS

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JOURNAL OF MOLECULAR BIOLOGY 1993年第4期232卷 1117-1129页

作者： YI, TM LANDER, ES MIT DEPT BIOLCAMBRIDGEMA 02142

We have studied the use of nearest-neighbor classifiers to predict the secondary structure of proteins. The nearest-neighbor rule states that a test instance is classified according to the classifications of "nearby" training examples from a database of known structures. In the context of secondary structure prediction, the test instances are windows of n consecutive residues, and the label is the secondary structure type (α-helix, β-strand, or coil) of the center position of the window. To define the neighborhood of a test instance, we employed a novel similarity metric based on the local structural environment scoring scheme of Bowie et al. In this manner, we have attempted to exploit the underlying structural similarity between segments of different proteins to aid in the prediction of secondary structure. Furthermore, in addition to using neighborhoods of fixed radius, we explored a modification of the standard nearest-neighbor algorithm that involved defining an "effective radius" for each exemplar by measuring its performance on a training set. Using these ideas, we achieved a peak prediction accuracy of 68%. Finally, we sought to improve the biological utility of secondary structure prediction by identifying the subset of the predictions that are most likely to be correct. Toward this end, we developed a nearest-neighbor estimator that produced not the traditional "one-state" prediction (α-helix, β-strand, or coil) but rather a probability distribution over the three states. It should be emphasized that this scheme estimates true probability values and that the resulting numbers are not pseudo-probability scores generated by simple normalization of the raw output of the predictor. Applying the mutual information statistic, we found that these probability triplets possess 58% more information than the one-state predictions. Furthermore, the probability estimates allow one to assign an a priori confidence level to the prediction at each residue. Using this app

关键词： nearest-neighbor algorithm SECONDARY STRUCTURE PREDICTION ARTIFICIAL INTELLIGENCE, PROTEIN STRUCTURE

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Dynamic feature weighting in nearest neighbor classifiers

Dynamic feature weighting in nearest neighbor classifiers

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International Conference on Machine Learning and Cybernetics

作者： Tong, X Öztürk, P Gu, M Norwegian Univ Sci & Technol Dept Comp & Informat Sci N-7030 Trondheim Norway

ISBN: (纸本)0780384032

One major problem of nearest neighbor (NN) algorithms Is inefficiency incurred by irrelevant features. A solution to this problem Is to assign weights to features to Indicate their salience for classification. Current weighting methods can be divided as global weighting, partial local weighting, and local weighting methods enumerated in increasing order of capability to capture features' relative salience in classification. However, existing methods are not sensitive enough to describe that the salience of a feature can be changed given different queries. We suggest that the salience of a feature, in addition to being sensitive to the Instance (i.e. varies across instances), should also be sensitive to the variations In the difference of a feature's values between a query and the instances In the instance base. In this paper, we put forward a dynamic feature weighting approach which has more expressive capability, and present a sketch of a classification algorithm based on the notion of dynamic weights.

关键词： nearest-neighbor algorithm feature weights dynamic weights classification

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A New Visual Inertial Simultaneous Localization and Mapping (SLAM) algorithm Based on Point and Line Features

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DRONES 2022年第1期6卷 23-23页

作者： Zhang, Tong Liu, Chunjiang Li, Jiaqi Pang, Minghui Wang, Mingang Northwestern Polytech Univ Unmanned Syst Res Inst Xian 710072 Peoples R China Syst Engn Inst Sichuan Aerosp Chengdu 610100 Peoples R China Northwestern Polytech Univ Sch Astronaut Xian 710072 Peoples R China

In view of traditional point-line feature visual inertial simultaneous localization and mapping (SLAM) system, which has weak performance in accuracy so that it cannot be processed in real time under the condition of weak indoor texture and light and shade change, this paper proposes an inertial SLAM method based on point-line vision for indoor weak texture and illumination. Firstly, based on Bilateral Filtering, we apply the Speeded Up Robust Features (SURF) point feature extraction and Fast nearest neighbor (FLANN) algorithms to improve the robustness of point feature extraction result. Secondly, we establish a minimum density threshold and length suppression parameter selection strategy of line feature, and take the geometric constraint line feature matching into consideration to improve the efficiency of processing line feature. And the parameters and biases of visual inertia are initialized based on maximum posterior estimation method. Finally, the simulation experiments are compared with the traditional tightly-coupled monocular visual-inertial odometry using point and line features (PL-VIO) algorithm. The simulation results demonstrate that the proposed an inertial SLAM method based on point-line vision for indoor weak texture and illumination can be effectively operated in real time, and its positioning accuracy is 22% higher on average and 40% higher in the scenario that illumination changes and blurred image.

关键词： simultaneous localization and mapping (SLAM) fast bilateral filtering SURF algorithm nearest-neighbor algorithm geometric constraints feature extraction

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