Search results for "Mining"
showing 10 items of 1730 documents
Using recursive Bayesian estimation for matching GPS measurements to imperfect road network data
2010
Map-matching refers to the process of projecting positioning measurements to a location on a digital road network map. It is an important element of intelligent transportation systems (ITS) focusing on driver assistance applications, on emergency and incident management, arterial and freeway management, and other applications. This paper addresses the problem of map-matching in the applications characterized by imperfect map quality and restricted computational resources - e.g. in the context of community-based ITS applications. Whereas a number of map-matching methods are available, often these methods rely on topological analysis, thereby making them sensitive to the map inaccuracies. In …
Validation of Semantic Analyses of Unstructured Medical Data for Research Purposes
2019
BACKGROUND: In secondary data there are often unstructured free texts. The aim of this study was to validate a text mining system to extract unstructured medical data for research purposes. METHODS: From a radiological department, 1,000 out of 7,102 CT findings were randomly selected. These were manually divided into defined groups by 2 physicians. For automated tagging and reporting, the text analysis software Averbis Extraction Platform (AEP) was used. Special features of the system are a morphological analysis for the decomposition of compound words as well as the recognition of noun phrases, abbreviations and negated statements. Based on the extracted standardized keywords, findings rep…
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…