Search results for "Data mining"

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…

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…

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…

Transcarotid approach for TAVI: an optimal alternative to the transfemoral gold standard

2017

Combining similarity measures in content-based image retrieval

2008

The purpose of content based image retrieval (CBIR) systems is to allow users to retrieve pictures from large image repositories. In a CBIR system, an image is usually represented as a set of low level descriptors from which a series of underlying similarity or distance functions are used to conveniently drive the different types of queries. Recent work deals with combination of distances or scores from different and usually independent representations in an attempt to induce high level semantics from the low level descriptors of the images. Choosing the best method to combine these results requires a careful analysis and, in most cases, the use of ad-hoc strategies. Combination based on or…

Semi-Supervised Remote Sensing Image Classification based on Clustering and the Mean Map Kernel

2008

This paper presents a semi-supervised classifier based on the combination of the expectation-maximization (EM) algorithm for Gaussian mixture models (GMM) and the mean map kernel. The proposed method uses the most reliable samples in terms of maximum likelihood to compute a kernel function that accurately reflects the similarity between clusters in the kernel space. The proposed method improves classification accuracy in situations where the available labeled information does not properly describe the classes in the test image.

Nonlocal van der Waals Approach Merged with Double-Hybrid Density Functionals: Toward the Accurate Treatment of Noncovalent Interactions

2015

Noncovalent interactions drive the self-assembly of weakly interacting molecular systems to form supramolecular aggregates, which play a major role in nanotechnology and biochemistry. In this work, we present a thorough assessment of the performance of different double-hybrid density functionals (PBE0-DH-NL, revPBE0-DH-NL, B2PLYP-NL, and TPSS0-DH-NL), as well as their parent hybrid and (meta)GGA functionals, in combination with the most modern version of the nonlocal (NL) van der Waals correction. It is shown that this nonlocal correction can be successfully coupled with double-hybrid density functionals thanks to the short-range attenuation parameter b, which has been optimized against ref…

Semantic place recognition for context aware services

2012

Extracting the meaning of the most significant places, which are frequently visited by a mobile user, is a relevant problem in mobile computing. Predicting semantic meaning of such places is useful in many areas. The problem of place semantic annotation of a user location can be challenging for service providers. Awareness of user activities is very important for development of personalized applications, which can be used in health care systems, living systems, etc. Predicting location of mobile users not only enables development of high quality location-based services and applications, but also improves resource reservation in wireless networks. In this research several solutions for seman…