An evaluation of Guided Regularized Random Forest for classification and regression tasks in remote sensing

New Earth observation missions and technologies are delivering large amounts of data. Processing this data requires developing and evaluating novel dimensionality reduction approaches to identify the most informative features for classification and regression tasks. Here we present an exhaustive evaluation of Guided Regularized Random Forest (GRRF), a feature selection method based on Random Forest. GRRF does not require fixing a priori the number of features to be selected or setting a threshold of the feature importance. Moreover, the use of regularization ensures that features selected by GRRF are non-redundant and representative. Our experiments based on various kinds of remote sensing images, show that GRRF selected features provides similar results to those obtained when using all the available features. However, the comparison between GRRF and standard random forest features shows substantial differences: in classification, the mean overall accuracy increases by almost 6% and, in regression, the decrease in RMSE almost reaches 2%. These results demonstrate the potential of GRRF for remote sensing image classification and regression. Especially in the context of increasingly large geodatabases that challenge the application of traditional methods.     

上扬子地台震旦系铅锌矿床类型及找矿方向

上扬子地台含铅锌矿地层主要为震旦系。震旦系在上扬子地台主要发育上统灯影组,次有下统陡山沱组。将上扬子地台震旦系铅锌矿床成因划分为一级为叠生矿床,二级为层控矿床,三级为热水沉积改造矿床和热水沉积再造矿床四级类型,该类型按铅锌比例进一步划分为富铅型、富锌型、铅锌共生型。研究区内最重要的铅锌矿床成因类型为南郑马元、会东大梁子铅锌矿为代表的富锌型。陕西南郑、紫阳—镇坪地区,湖北武当、神农架、黄陵地区,以及地台西缘的安宁河、甘洛—小江、峨边—寻甸等地区的震旦系是寻找铅锌矿的最有利地段。     

用遗传算法对自动控制系统数学模型寻优

探讨用遗传算法对数学模型进行优化。考虑到控制系统稳健性的要求 ,用遗传算法寻找出控制系统最佳稳定域 ,实现控制系统数学模型的寻优。阐述了用遗传算法求解问题的步骤和参数的取值 ,并用仿真实验对优化结果进行了检验。结果表明 :在控制系统数学模型的优化中 ,遗传算法具有其他算法无可比拟的优越性。     

Prediction of Stellar Atmospheric Parameters using Instance-Based Machine Learning and Genetic Algorithms

In this article we present a method for the automated prediction of stellar atmospheric parameters from spectral indices. This method uses a genetic algorithm (GA) for the selection of relevant spectral indices and prototypical stars and predicts their properties, using the k-nearest neighbors method (KNN). We have applied the method to predict the effective temperature, surface gravity, metallicity, luminosity class and spectral class of stars from spectral indices. Our experimental results show that the feature selection performed by the genetic algorithm reduces the running time of KNN up to 92%, and the predictive accuracy error up to 35%. This revised version was published online in July 2006 with corrections to the Cover Date.     

Properties of Galaxies in the Second Byurakan Survey. II. Characteristic Features of a Separate Sample of Galaxies

Some features of a sample of galaxies from the Second Byurakan Survey are discussed. Most of them are small galaxies with star formation. It is shown that different types of galaxies are in the Survey, with high, medium, and low levels of excitation and continua ranging from blue to relatively red. Many of the galaxies have absorption lines in their spectra; their continua are formed by stars in later spectral classes.     

AN INTEGRATED APPROACH TO AIR MASS CLASSIFICATION IN THE NORTH CENTRAL UNITED STATES

Both subjective and objective schemes for air mass classification offer advantages to climatological studies. An integrated approach is developed and applied to 850 mb level data in the north central United States. Each air mass type was initially delimited by a subjective analysis. Final limits were then determined with an objective technique that separates temperature curves into normal components based on interpretation from the subjective method. Continental air was most frequent in all seasons except summer. The methodology also allows estimation of classification error.     

Discrimination of sedimentary facies by association analysis

The ability of association analysis to discriminate sedimentary facies was tested on Purdy's modal analyses of modern sediments of the Great Bahama Bank. Purdy's data set has served in the past as a standard reference for evaluating various multivariate classification algorithms. In order to adapt Purdy's data to association analysis, the percent abundance of the 12 constituents was converted to binary form by dichotomizing each variable on its mean value. The results obtained by association analysis are virtually identical to those obtained by Purdy and other authors. The same four main sedimentary facies were discriminated; 86% of the samples were identically classified (97% when misclassified borderline cases are counted as matches); the total partition variance of the classification is only negligibly greater (4%); and the grouping of the variables yielded the same four groups. The rank order of the three division-attributes responsible for the sample classification is fines, oolites, and corals. Association analysis has been employed by other authors to differentiate meaningful facies groups in studies of ancient reef carbonates, modern reef sediments, and heavy minerals in stream sediments. In all these studies, the results were found to be compatible with those obtained by using the continuous quantitative measurements, indicating that qualitative binary data may often be sufficient for the purpose of facies discrimination in many branches of geology and that association analysis is an effective method for this purpose.     

用遗传算法解算病态方程

对应用遗传算法解决病态方程问题进行了探讨。利用拟合法而不是通过法方程求解参数，从而避免了法方程系数求逆，使病态方程的解答有了较好的结果。通过模拟计算并和其他方法进行比较，证明该方法是可行的和有效的。     

The comparison between allozyme and RAPD makers for the population genetic structure analysis of scallop Chlamys farreri

1INTRODUCTION Since the first use in human genetic research in1966,allozyme has become the most popular genetic marker for population genetics studies for almost all groups of animals and plants(Lewontin and Hubby,1966;Murphy et al.,1996).In the past seve…     

THE EVOLUTION OF SERVICE ECONOMIES: A GEOGRAPHICAL PERSPECTIVE1

Of the several classifications of services, each emphasizes different characteristics. The International Standard Industrial Classification (ISIC) is used for a comparative study on the transformation into service economies of the U.S., Canada, Great Britain, France, West Germany, Italy and Japan. Service economies are defined as national economies in which employment in services surpassed that in manufacturing. The pace of the evolution of service economies has been rapid with North America leading, followed in order by Great Britain, France, Italy, Japan and West Germany. Service economies have important implications for urban growth, intercity competition, and international trade.