Search results for "Knowledge extraction"
showing 10 items of 58 documents
Diversity in random subspacing ensembles
2004
Ensembles of learnt models constitute one of the main current directions in machine learning and data mining. It was shown experimentally and theoretically that in order for an ensemble to be effective, it should consist of classifiers having diversity in their predictions. A number of ways are known to quantify diversity in ensembles, but little research has been done about their appropriateness. In this paper, we compare eight measures of the ensemble diversity with regard to their correlation with the accuracy improvement due to ensembles. We conduct experiments on 21 data sets from the UCI machine learning repository, comparing the correlations for random subspacing ensembles with diffe…
A Special Issue on Advances in Machine Learning for Remote Sensing and Geosciences [From the Guest Editors]
2016
Machine learning has become a standard paradigm for the analysis of remote sensing and geoscience data at both local and global scales. In the upcoming years, with the advent of new satellite constellations, machine learning will have a fundamental role in processing large and heterogeneous data sources. Machine learning will move from mere statistical data processing to actual learning, understanding, and knowledge extraction. The ambitious goal is to provide responses to the challenging scientific questions about the earth system. This special issue aims at providing an updated, refreshing view of current developments in the field. For this special issue, we have collected five articles t…
Knowledge Extraction from Environmental Data Through a Cognitive Architecture
2008
Wireless Sensor Networks represent a novel technology which is expected to experience a dramatic diffusion thanks to the promise to be a pervasive sensory means; however, one of the issues limiting their potential growth relies in the difficulty of managing and interpreting huge amounts of collected data. This paper proposes a cognitive architecture for the extraction of high-level knowledge from raw data through the representation of processed data in opportune conceptual spaces. The presented framework interposes a conceptual layer between the subsymbolic one, devoted to sensory data processing, and the symbolic one, aimed at describing the environment by means of a high level language. T…
Analysis of multi-source metabolomic data using joint and individual variation explained (JIVE).
2015
Metabolic profiling is increasingly being used for understanding biological processes but there is no single analytical technique that provides a complete quantitative or qualitative profiling of the metabolome. Data fusion (i.e. joint analysis of data from multiple sources) has the potential to circumvent this issue facilitating knowledge discovery and reliable biomarker identification. Another field of application of data fusion is the simultaneous analysis of metabolomic changes through several biofluids or tissues. However, metabolomics typically deals with large datasets, with hundreds to thousands of variables and the identification of shared and individual factors or structures acros…
LC3: A spatio-temporal and semantic model for knowledge discovery from geospatial datasets
2015
International audience; There is a need for decision-makers to be provided with both an overview of existing knowledge, and information which is as complete and up-to-date as possible on changes in certain features of the biosphere. Another objective is to bring together all the many attempts which have been made over the years at various levels (international, Community, national and regional) to obtain more information on the environment and the way it is changing. As a result, remote sensing tools monitor large amount of land cover informations enabling study of dynamic processes. However the size of the dataset require new tools to identify pattern and extract knowledge. We propose a mo…
A Conceptual Architecture of Ontology Based KM System for Failure Mode and Effects Analysis
2014
Failure Mode and Effects Analysis (FMEA) is a systematic method for procedure analyses and risk assessment. It is a structured way to identify potential failure modes of a product or process, probability of their occurrence, and their overall effects. The basic purpose of this analysis is to mitigate the risk and the impact associated to a failure by planning and prioritizing actions to make a product or a process robust to failure. Effective manufacturing and improved quality products are the fruits of successful implementation of FMEA. During this activity valuable knowledge is generated which turns into product or process quality and efficiency. If this knowledge can be shared and reused…
Feature extraction for classification in knowledge discovery systems
2003
Dimensionality reduction is a very important step in the data mining process. In this paper, we consider feature extraction for classification tasks as a technique to overcome problems occurring because of "the curse of dimensionality". We consider three different eigenvector-based feature extraction approaches for classification. The summary of obtained results concerning the accuracy of classification schemes is presented and the issue of search for the most appropriate feature extraction method for a given data set is considered. A decision support system to aid in the integration of the feature extraction and classification processes is proposed. The goals and requirements set for the d…
Ontological supports of knowledge: knowledge creation and analytical knowledge
2011
PurposeThe purpose of this paper is to examine, from the perspective of different theoretical approaches, the relationship that exists between different ontological supports of knowledge and knowledge itself (the way it is created and its characteristics).Design/methodology/approachThe paper proposes two different types of knowledge (knowledge of concrete situations and abstract knowledge) and two approaches (the constructivist view and the cognitive view) and provides a general classification of the different knowledge types. Second, it examines the underlying ontological support‐knowledge creation, characteristics or types of knowledge relationship in different approaches. Finally, conclu…
Can Signaling Theory Help Agency and Resource Scarcity Theories Explain Franchisee Failure? Predicting SBA-Backed Loan Defaults
2010
This study examines the use of analytic techniques to develop a model that predicts the potential default of Small Business Administration (SBA) backed loans issued to American franchisees. Data collected by World Franchising (WF) in their 2008 survey and by SBA from 2000-2008, covering 271 diverse US franchise chains, on the reported failure rates and charge off percentages of SBA backed loans was used to explore associations between franchisor characteristics and franchisee loan performances. The predictive capability of the derived model was assessed using a data mining technique in which the original data set is split into two different subsets: one for estimation and one for validation…
Data Mining Algorithms for Knowledge Extraction
2020
In this paper, we study the methods, techniques, and algorithms used in data mining, and from the studied algorithms, we emphasized the clustering algorithms, more precisely on the K-means algorithm. This algorithm was first studied using the Euclidean distance, then modifying the distance between the clusters using the distances Mahalanobis and Canberra. After implementing the algorithms in C/C++, we compared the clustering of the three algorithms, after which we modified them and studied the distance between the clusters.