Search results for " mining"

Spectro-temporal reflectance surfaces: a new conceptual framework for the integration of remote-sensing data from multiple different sensors

2012

The conflict between spatial and temporal resolution of satellite systems, as well as the frequent presence of clouds in the images, has been a traditional limitation of remote sensing in the optical domain. Nevertheless, most of the conceptual tools and algorithms developed classically in remote sensing are based on the input of a series of cloud-free images from identical sensors. In this study, we propose a conceptual framework that is able to ingest data from several different sensors, make them homogeneous, eliminate clouds virtually, and make them usable in a flexible, efficient, and transparent way. The methodology is based on previous developments such as spatial ‘downscaling’, temp…

Special issue on pattern recognition techniques in data mining

2017

Peer Reviewed

Diversity in search strategies for ensemble feature selection

2005

Ensembles of learnt models constitute one of the main current directions in machine learning and data mining. Ensembles allow us to achieve higher accuracy, which is often not achievable with single models. It was shown theoretically and experimentally that in order for an ensemble to be effective, it should consist of base classifiers that have diversity in their predictions. One technique, which proved to be effective for constructing an ensemble of diverse base classifiers, is the use of different feature subsets, or so-called ensemble feature selection. Many ensemble feature selection strategies incorporate diversity as an objective in the search for the best collection of feature subse…

<title>Distance functions in dynamic integration of data mining techniques</title>

2000

One of the most important directions in the improvement of data mining and knowledge discovery is the integration of multiple data mining techniques. An integration method needs to be able either to evaluate and select the most appropriate data mining technique or to combine two or more techniques efficiently. A recent integration method for the dynamic integration of multiple data mining techniques is based on the assumption that each of the data mining techniques is the best one inside a certain subarea of the whole domain area. This method uses an instance-based learning approach to collect information about the competence areas of the mining techniques and applies a distance function to…

Improving big-data automotive applications performance through adaptive resource allocation

2019

In automotive applications, connected vehicles (CVs) can collect various information (external temperature, speed, location, etc.) and send them to a central infrastructure for exploitation in a wide range of applications: Eco-Driving, fleet management, environmental monitoring, etc. Such applications are known to generate a massive volume of data that is processed in real or near real time (i.e., data streams) depending on the target application requirements. To handle this data volume, big data architectures, based on stream computing paradigm, are usually adopted. Within this paradigm, data are continuously processed by a set of operators (elementary operations) instances. Further, a str…

F.A.L.C.A.D.E.: a fuzzy software for the energy and environmental balances of products

2004

Abstract It is generally well known that the reliability of Life Cycle Analysis (LCA) studies depends upon exact, complete and sharp input data that, unfortunately, are not always available. Furthermore, when available, the input data are affected by uncertainty whose importance is not always adequately taken into consideration. This paper describes the software F.A.L.C.A.D.E. (Fuzzy Approach to Life Cycle Analysis and Decision Environment): a tool designed for the calculation of the eco-profile of products, based on a fuzzy logic approach. The originality of the method already treated in other papers is to use the fuzzy representation to manage the complex relationships that arise in compi…

Semisupervised Kernel Feature Extraction for Remote Sensing Image Analysis

2014

This paper presents a novel semisupervised kernel partial least squares (KPLS) algorithm for nonlinear feature extraction to tackle both land-cover classification and biophysical parameter retrieval problems. The proposed method finds projections of the original input data that align with the target variable (labels) and incorporates the wealth of unlabeled information to deal with low-sized or underrepresented data sets. The method relies on combining two kernel functions: the standard radial-basis-function kernel based on labeled information and a generative, i.e., probabilistic, kernel directly learned by clustering the data many times and at different scales across the data manifold. Th…

Cortical Hypoactivity or Reduced Efficiency of Cortical Inhibition in Migraine?

2007

New glucocorticoids. Mechanisms of immunological activity at the cellular level and in the clinical setting.

1990

Applying logistic regression to relevance feedback in image retrieval systems

2007

This paper deals with the problem of image retrieval from large image databases. A particularly interesting problem is the retrieval of all images which are similar to one in the user's mind, taking into account his/her feedback which is expressed as positive or negative preferences for the images that the system progressively shows during the search. Here we present a novel algorithm for the incorporation of user preferences in an image retrieval system based exclusively on the visual content of the image, which is stored as a vector of low-level features. The algorithm considers the probability of an image belonging to the set of those sought by the user, and models the logit of this prob…