Search results for "Mining"

Predictive and Contextual Feature Separation for Bayesian Metanetworks

2007

Bayesian Networks are proven to be a comprehensive model to describe causal relationships among domain attributes with probabilistic measure of conditional dependency. However, depending on a context, many attributes of the model might not be relevant. If a Bayesian Network has been learned across multiple contexts then all uncovered conditional dependencies are averaged over all contexts and cannot guarantee high predictive accuracy when applied to a concrete case. We are considering a context as a set of contextual attributes, which are not directly effect probability distribution of the target attributes, but they effect on "relevance" of the predictive attributes towards target attribut…

Comparative evaluation of data preprocessing software tools to increase efficiency and accuracy in diffusion kurtosis imaging

2016

Do we need metamodels AND ontologies for engineering platforms?

2006

In this paper we show how the joint use of metamodeling and ontologies allows to describe domain knowledge for a complex domain. Ontologies are used as stabilized descriptions of a business domain while metamodels allow a fine description of the domain (to be constructed in the initial phases of modeling). We propose to use an ontology for early categorization, i.e., as a "natural" complement of the formal system that is induced by the metamodel.

A Pattern Recognition Approach for Peak Prediction of Electrical Consumption

2014

Predicting and mitigating demand peaks in electrical networks has become a prevalent research topic. Demand peaks pose a particular challenge to energy companies because these are difficult to foresee and require the net to support abnormally high consumption levels. In smart energy grids, time-differentiated pricing policies that increase the energy cost for the consumers during peak periods, and load balancing are examples of simple techniques for peak regulation. In this paper, we tackle the task of predicting power peaks prior to their actual occurrence in the context of a pilot Norwegian smart grid network.

Feature extraction from remote sensing data using Kernel Orthonormalized PLS

2007

This paper presents the study of a sparse kernel-based method for non-linear feature extraction in the context of remote sensing classification and regression problems. The so-called kernel orthonormalized PLS algorithm with reduced complexity (rKOPLS) has two core parts: (i) a kernel version of OPLS (called KOPLS), and (ii) a sparse (reduced) approximation for large scale data sets, which ultimately leads to rKOPLS. The method demonstrates good capabilities in terms of expressive power of the extracted features and scalability.

Local dimensionality reduction within natural clusters for medical data analysis

2005

Inductive learning systems have been successfully applied in a number of medical domains. Nevertheless, the effective use of these systems requires data preprocessing before applying a learning algorithm. Especially it is important for multidimensional heterogeneous data, presented by a large number of features of different types. Dimensionality reduction is one commonly applied approach. The goal of this paper is to study the impact of natural clustering on dimensionality reduction for classification. We compare several data mining strategies that apply dimensionality reduction by means of feature extraction or feature selection for subsequent classification. We show experimentally on micr…

Extracting information from support vector machines for pattern-based classification

2014

Statistical machine learning algorithms building on patterns found by pattern mining algorithms have to cope with large solution sets and thus the high dimensionality of the feature space. Vice versa, pattern mining algorithms are frequently applied to irrelevant instances, thus causing noise in the output. Solution sets of pattern mining algorithms also typically grow with increasing input datasets. The paper proposes an approach to overcome these limitations. The approach extracts information from trained support vector machines, in particular their support vectors and their relevance according to their coefficients. It uses the support vectors along with their coefficients as input to pa…

Improving distance based image retrieval using non-dominated sorting genetic algorithm

2015

Image retrieval is formulated as a multiobjective optimization problem.A multiobjective genetic algorithm is hybridized with distance based search.A parameter balances exploration (genetic search) or exploitation (nearest neighbors).Extensive comparative experimentation illustrate and assess the proposed methodology. Relevance feedback has been adopted as a standard in Content Based Image Retrieval (CBIR). One major difficulty that algorithms have to face is to achieve and adequate balance between the exploitation of already known areas of interest and the exploration of the feature space to find other relevant areas. In this paper, we evaluate different ways to combine two existing relevan…

Cognitive intelligent sensory system for vision-based quality control

2003

This paper presents an original approach for a vision-based quality control system, built around a cognitive intelligent sensory system. The principle of the approach relies on two steps. First, a so-called initialization phase leads to structural knowledge on image acquisition conditions, type of illumination sources, etc. Second, the image is iteratively evaluated using this knowledge and complementary information (e.g., CAD models, and tolerance information). Finally, the information describing the quality of the piece under evaluation is extracted. A further aim of the approach is to enable building strategies that determine for instance the “next best view” required for completing the …

An Agents and Artifacts Approach to Distributed Data Mining

2013

This paper proposes a novel Distributed Data Mining (DDM) approach based on the Agents and Artifacts paradigm, as implemented in CArtAgO [9], where artifacts encapsulate data mining tools, inherited from Weka, that agents can use while engaged in collaborative, distributed learning processes. Target hypothesis are currently constrained to decision trees built with J48, but the approach is flexible enough to allow different kinds of learning models. The twofold contribution of this work includes: i) JaCA-DDM: an extensible tool implemented in the agent oriented programming language Jason [2] and CArtAgO [10,9] to experiment DDM agent-based approaches on different, well known training sets. A…