Binarized support vector machines

The widely used Support Vector Machine (SVM) method has shown to yield very good results in Supervised Classification problems. Other methods such as Classification Trees have become more popular among practitioners than SVM thanks to their interpretability, which is an important issue in Data Mining. In this work, we propose an SVM-based method that automatically detects the most important predictor variables, and the role they play in the classifier. In particular, the proposed method is able to detect those values and intervals which are critical for the classification. The method involves the optimization of a Linear Programming problem, with a large number of decision variables. The numerical experience reported shows that a rather direct use of the standard Column-Generation strategy leads to a classification method which, in terms of classification ability, is competitive against the standard linear SVM and Classification Trees. Moreover, the proposed method is robust, i.e., it is stable in the presence of outliers and invariant to change of scale or measurement units of the predictor variables. When the complexity of the classifier is an important issue, a wrapper feature selection method is applied, yielding simpler, still competitive, classifiers

copulaedas: An R Package for Estimation of Distribution Algorithms Based on Copulas

The use of copula-based models in EDAs (estimation of distribution algorithms) is currently an active area of research. In this context, the copulaedas package for R provides a platform where EDAs based on copulas can be implemented and studied. The package offers complete implementations of various EDAs based on copulas and vines, a group of well-known optimization problems, and utility functions to study the performance of the algorithms. Newly developed EDAs can be easily integrated into the package by extending an S4 class with generic functions for their main components. This paper presents copulaedas by providing an overview of EDAs based on copulas, a description of the implementation of the package, and an illustration of its use through examples. The examples include running the EDAs defined in the package, implementing new algorithms, and performing an empirical study to compare the behavior of different algorithms on benchmark functions and a real-world problem

Evolution of postglacial vegetation in the Western Laptev Sea region (Siberian Arctic)

On the basis of a detailed study of the pollen-spore spectra and a detailed radiocarbon chronology of a sediment core obtained from the western outer Laptev Sea shelf, the long-term and high-resolution changes of vegetation in the northwestern Laptev Sea region were reconstructed for the last 12.0 cal. ka. Three major phases in the development of paleoenvironments and vegetation on the surrounding hinterland and the exposed Laptev Sea shelf were recognized. The period between 12.0 and 10.3 cal. ka BP was characterized by predominance of grass-sedge and moss tundra. Rapid expansion of herbaceous tundra with dwarf birch and alder started at about 10.3 and lasted until 8.0 cal. ka. Pollen spectra from this time interval evidence the warmest and most favorable climate conditions. After 8.0 cal. ka mosses and lichen vegetation with scare herbs typical for the modern arctic tundra dominated. German: Auf der Grundlage detaillierter Pollen- und Sporenspektren aus einem 14C-datierten Sedimentkern vom äußeren Schelf des westlichen Laptewmeeres wurden die langfristigen und hochaufgelösten Veränderungen der Vegetation in den letzten 12 cal. ka in der nordwestlichen Laptewmeer-Region rekonstruiert. Es wurden drei Hauptphasen der Entwicklung von Umwelt und Vegetation im umgebenden Hinterland erkannt. Die Zeit zwischen 12,0 und 10,3 cal. ka BP war charakterisiert durch Riedgras- und Moos-Tundra. Rasche Ausdehnung der Kraut-Tundra mit Zwergbirke und Erle begann etwa um 10,3 cal. ka und dauerte bis 8,0 cal. ka. Pollenspektren aus diesem Zeitintervall beschreiben die wärmsten und besten Klimabedingungen. Nach 8,0 cal. ka dominierte die für die heutige arktische Tundra typische Moos- und Flechtenvegetation mit wenigen Kräutern

Binarized support vector machines

The widely used Support Vector Machine (SVM) method has shown to yield very good results in Supervised Classification problems. Other methods such as Classification Trees have become more popular among practitioners than SVM thanks to their interpretability, which is an important issue in Data Mining. In this work, we propose an SVM-based method that automatically detects the most important predictor variables, and the role they play in the classifier. In particular, the proposed method is able to detect those values and intervals which are critical for the classification. The method involves the optimization of a Linear Programming problem, with a large number of decision variables. The numerical experience reported shows that a rather direct use of the standard Column-Generation strategy leads to a classification method which, in terms of classification ability, is competitive against the standard linear SVM and Classification Trees. Moreover, the proposed method is robust, i.e., it is stable in the presence of outliers and invariant to change of scale or measurement units of the predictor variables. When the complexity of the classifier is an important issue, a wrapper feature selection method is applied, yielding simpler, still competitive, classifiers.Supervised classification, Binarization, Column generation, Support vector machines