Feature selection methods for remote sensing images classification
Different methods of feature selection are used to improve the performance of remote sensing images classification. In this work two methods of feature selection are examined. The first one is based on the discriminant analysis, and the second one rests on building the regression model. Histogram and textural features are considered as characteristics of an image. The experiments on the remote sensing dataset UC Merced Land Use show the effectiveness of these methods. As the result, the largest fraction of correctly classified images accounts for the 95%. Dimension of the initial feature space consisting of 18 features has been reduced to 3 features.
The article gives an overview of the main approaches to verification of the internal rating methods of analysis of the creditworthiness. The article gives practical advice on the application of these techniques by an example of real internal rating technique.
This book constitutes the thoroughly refereed post-conference proceedings of the 8th International Conference on Learning and Optimization, LION 8, which was held in Gainesville, FL, USA, in February 2014. The 33 contributions presented were carefully reviewed and selected for inclusion in this book. A large variety of topics are covered, such as algorithm configuration; multiobjective optimization; metaheuristics; graphs and networks; logistics and transportation; and biomedical applications.
The paper makes a brief introduction into multiple classifier systems and describes a particular algorithm which improves classification accuracy by making a recommendation of an algorithm to an object. This recommendation is done under a hypothesis that a classifier is likely to predict the label of the object correctly if it has correctly classified its neighbors. The process of assigning a classifier to each object involves here the apparatus of Formal Concept Analysis. We explain the principle of the algorithm on a toy example and describe experiments with real-world datasets.
Symbolic classifiers allow for solving classification task and provide the reason for the classifier decision. Such classifiers were studied by a large number of researchers and known under a number of names including tests, JSM-hypotheses, version spaces, emerging patterns, proper predictors of a target class, representative sets etc. Here we consider such classifiers with restriction on counter-examples and discuss them in terms of pattern structures. We show how such classifiers are related. In particular, we discuss the equivalence between good maximally redundant tests and minimal JSM-hyposethes and between minimal representations of version spaces and good irredundant tests.
In this paper, we use robust optimization models to formulate the support vector machines (SVMs) with polyhedral uncertainties of the input data points. The formulations in our models are nonlinear and we use Lagrange multipliers to give the first-order optimality conditions and reformulation methods to solve these problems. In addition, we have proposed the models for transductive SVMs with input uncertainties.
The article explores the procedural aspect of constructing structural and logical typologies with the aim of creating the innovation index - workers attitudes guiding innovation and innovation -related behavior at workplace.