In the last decades, many efforts have been made to solve multimodal optimization problems using Particle Swarm Optimization (PSO). To produce good results, these PSO algorithms need to specify some niching parameters to define the local neighborhood. In this paper, our motivation is to propose the novel neighborhood structures that remove undesirable niching parameters without sacrificing perf...

For classification of time series, the simple 1-nearest neighbor (1NN) classifier in combination with an elastic distance measure such as Dynamic Time Warping (DTW) distance is considered superior in terms of classification accuracy to many other more elaborate methods, including k-nearest neighbor (kNN) with neighborhood size k > 1. In this paper we revisit this apparently peculiar relationshi...

Little is known about spatial inequalities and potential access to the food environment in rural areas. In this study, we assessed the food environment in a 6-county rural region of Texas (11,567 km2) through ground-truthed methods that included direct observation and on-site Global Positioning System technology to examine the relationship between neighborhood inequalities (e.g., socioeconomic ...

the main goal of the present study was to implement a hybrid pattern of cellular automata model and particle swarm optimization algorithm based on tm and etm+ imagery of landsat satellite from 1988 to 2010 for simulating the urban sprawling. in this study, an alternative model was implemented in two ways: the first method was based on two images (1988 and 2010) and the second one was based on t...

The spatio-temporal (ST) position information between local features plays an important role in action recognition task. To use the information, neighborhood-based features are built for describing local ST information around ST interest points. However, traditional methods of constructing neighborhood, such as sub-ST volumetric method and nearest-neighbor-based neighborhood method, ignore the ...

In this paper, a data mining approach is proposed for duration prediction of the town trips (travel time) in New York City. In this regard, at first, two novel approaches, including a mathematical and a statistical approach, are proposed for grouping categorical variables with a huge number of levels. The proposed approaches work based on the cost matrix generated by repetitive post-hoc tests f...

OBJECTIVE To assess the association between proximity to a needle exchange program (NEP) and experience of violence in an inner city neighborhood. METHODS A cross-sectional survey of residents of Harlem, New York City provided data for this study. We used geocoding to obtain the distance between respondents and the nearest NEP. Respondents reported violence they were aware of or had personall...

Nearest neighbor (NN) classification assumes locally constant class conditional probabilities, and suffers from bias in high dimensions with a small sample set. In this paper, we propose a novel cam weighted distance to ameliorate the curse of dimensionality. Different from the existing neighborhood-based methods which only analyze a small space emanating from the query sample, the proposed nea...

OBJECTIVE To determine the extent to which neighborhood needs (socioeconomic deprivation and vehicle availability) are associated with two criteria of food environment access: 1) distance to the nearest food store and fast food restaurant and 2) coverage (number) of food stores and fast food restaurants within a specified network distance of neighborhood areas of colonias, using ground-truthed ...

Sample weighting and variations in neighborhood or data-dependent distance metric definitions are three principal directions considered for improving k-NN classification technique. Recently, manifold-based distance metrics attracted considerable interest and computationally less demanding approximations are developed. However, a careful comparison of these alternative approaches is missing. In ...