1,213 research outputs found
Soft computing techniques: Theory and application for pattern classification
Master'sMASTER OF ENGINEERIN
Uncertainty Management of Intelligent Feature Selection in Wireless Sensor Networks
Wireless sensor networks (WSN) are envisioned to revolutionize the paradigm of monitoring complex real-world systems at a very high resolution. However, the deployment of a large number of unattended sensor nodes in hostile environments, frequent changes of environment dynamics, and severe resource constraints pose uncertainties and limit the potential use of WSN in complex real-world applications. Although uncertainty management in Artificial Intelligence (AI) is well developed and well investigated, its implications in wireless sensor environments are inadequately addressed. This dissertation addresses uncertainty management issues of spatio-temporal patterns generated from sensor data. It provides a framework for characterizing spatio-temporal pattern in WSN. Using rough set theory and temporal reasoning a novel formalism has been developed to characterize and quantify the uncertainties in predicting spatio-temporal patterns from sensor data. This research also uncovers the trade-off among the uncertainty measures, which can be used to develop a multi-objective optimization model for real-time decision making in sensor data aggregation and samplin
Intelligent Assistance for the Data Mining Process: An Ontology-based Approach
A data mining (DM) process involves multiple stages. A simple, but typical, process might include
preprocessing data, applying a data-mining algorithm, and postprocessing the mining results. There
are many possible choices for each stage, and only some combinations are valid. Because of the
large space and non-trivial interactions, both novices and data-mining specialists need assistance in
composing and selecting DM processes. We present the concept of Intelligent Discovery Assistants
(IDAs), which provide users with (i) systematic enumerations of valid DM processes, in order that
important, potentially fruitful options are not overlooked, and (ii) effective rankings of these valid
processes by different criteria, to facilitate the choice of DM processes to execute. We use a prototype
to show that an IDA can indeed provide useful enumerations and effective rankings. We discuss
how an IDA is an important tool for knowledge sharing among a team of data miners. Finally,
we illustrate all the claims with a comprehensive demonstration using a more involved process and
data from the 1998 KDDCUP competition.Information Systems Working Papers Serie
Intelligent Assistance for the Data Mining Process: An Ontology-based Approach
A data mining (DM) process involves multiple stages. A simple, but typical, process might include
preprocessing data, applying a data-mining algorithm, and postprocessing the mining results. There
are many possible choices for each stage, and only some combinations are valid. Because of the
large space and non-trivial interactions, both novices and data-mining specialists need assistance in
composing and selecting DM processes. We present the concept of Intelligent Discovery Assistants
(IDAs), which provide users with (i) systematic enumerations of valid DM processes, in order that
important, potentially fruitful options are not overlooked, and (ii) effective rankings of these valid
processes by different criteria, to facilitate the choice of DM processes to execute. We use a prototype
to show that an IDA can indeed provide useful enumerations and effective rankings. We discuss
how an IDA is an important tool for knowledge sharing among a team of data miners. Finally,
we illustrate all the claims with a comprehensive demonstration using a more involved process and
data from the 1998 KDDCUP competition.Information Systems Working Papers Serie
Towards Intelligent Assistance for a Data Mining Process:-
A data mining (DM) process involves multiple stages. A simple, but typical, process might include
preprocessing data, applying a data-mining algorithm, and postprocessing the mining results.
There are many possible choices for each stage, and only some combinations are valid.
Because of the large space and non-trivial interactions, both novices and data-mining specialists
need assistance in composing and selecting DM processes. Extending notions developed for
statistical expert systems we present a prototype Intelligent Discovery Assistant (IDA), which
provides users with (i) systematic enumerations of valid DM processes, in order that important,
potentially fruitful options are not overlooked, and (ii) effective rankings of these valid processes
by different criteria, to facilitate the choice of DM processes to execute. We use the prototype to
show that an IDA can indeed provide useful enumerations and effective rankings in the context
of simple classification processes. We discuss how an IDA could be an important tool for
knowledge sharing among a team of data miners. Finally, we illustrate the claims with a comprehensive
demonstration of cost-sensitive classification using a more involved process and data
from the 1998 KDDCUP competition.NYU, Stern School of Business, IOMS Department, Center for Digital Economy Researc
Toward Intelligent Assistance for a Data Mining Process: An Ontology-Based Approach for Cost-Sensitive Classification
A data mining (DM) process involves multiple stages. A simple, but typical, process might include preprocessing data, applying a data mining algorithm, and postprocessing the mining results. There are many possible choices for each stage, and only some combinations are valid. Because of the large space and nontrivial interactions, both novices and data mining specialists need assistance in composing and selecting DM processes. Extending notions developed for statistical expert systems we present a prototype intelligent discovery assistant (IDA), which provides users with 1) systematic enumerations of valid DM processes, in order that important, potentially fruitful options are not overlooked, and 2) effective rankings of these valid processes by different criteria, to facilitate the choice of DM processes to execute. We use the prototype to show that an IDA can indeed provide useful enumerations and effective rankings in the context of simple classification processes. We discuss how an IDA could be an important tool for knowledge sharing among a team of data miners. Finally, we illustrate the claims with a demonstration of cost-sensitive classification using a more complicated process and data from the 1998 KDDCUP competition
Topological and algebraic characterization of coverings sets obtained in rough sets discretization and attribute reduction algorithms
Abstract. A systematic study on approximation operators in covering based rough sets and some relations with relation based rough sets are presented. Two different frameworks of approximation operators in covering based rough sets were unified in a general framework of dual pairs. This work establishes some relationships between the most important generalization of rough set theory: Covering based and relation based rough sets. A structured genetic algorithm to discretize, to find reducts and to select approximation operators for classification problems is presented.Se presenta un estudio sistemático de los diferentes operadores de aproximación en conjuntos aproximados basados en cubrimientos y operadores de aproximación basados en relaciones binarias. Se unifican dos marcos de referencia sobre operadores de aproximación basados en cubrimientos en un único marco de referencia con pares duales. Se establecen algunas relaciones entre operadores de aproximación de dos de las más importantes generalizaciones de la teoría de conjuntos aproximados. Finalmente, se presenta un algoritmo genético estructurado, para discretizar, reducir atributos y seleccionar operadores de aproximación, en problemas de clasificación.Doctorad
Toward an optimal solver for the obstacle problem
Master's Project (M.S.) University of Alaska Fairbanks, 2018An optimal algorithm for solving a problem with m degrees of freedom is one that computes a solution in O (m) time. In this paper, we discuss a class of optimal algorithms for the numerical solution of PDEs called multigrid methods. We go on to examine numerical solvers for the obstacle problem, a constrained PDE, with the goal of demonstrating optimality. We discuss two known algorithms, the so-called reduced space method (RSP) [BM03] and the multigrid-based projected full-approximation scheme (PFAS) [BC83]. We compare the performance of PFAS and RSP on a few example problems, finding numerical evidence of optimality or near-optimality for PFAS
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