113 research outputs found
Induction of formal concepts by lattice computing techniques for tunable classification
This work proposes an enhancement of Formal Concept Analysis (FCA) by Lattice Computing (LC) techniques. More
specifically, a novel Galois connection is introduced toward defining tunable metric distances as well as tunable
inclusion measure functions between formal concepts induced from hybrid (i.e., nominal and numerical) data. An
induction of formal concepts is pursued here by a novel extension of the Karnaugh map, or K-map for short, technique
from digital electronics. In conclusion, granular classification can be pursued. The capacity of a classifier based on
formal concepts is demonstrated here with promising results. The formal concepts are interpreted as descriptive decisionmaking
knowledge (rules) induced from the training data
Optimized classification predictions with a new index combining machine learning algorithms
Voting is a commonly used ensemble method aiming to optimize classification predictions by combining results from individual base classifiers. However, the selection of appropriate classifiers to participate in voting algorithm is currently an open issue. In this study we developed a novel Dissimilarity-Performance (DP) index which incorporates two important criteria for the selection of base classifiers to participate in voting: their differential response in classification (dissimilarity) when combined in triads and their individual performance. To develop this empirical index we firstly used a range of different datasets to evaluate the relationship between voting results and measures of dissimilarity among classifiers of different types (rules, trees, lazy classifiers, functions and Bayes). Secondly, we computed the combined effect on voting performance of classifiers with different individual performance and/or diverse results in the voting performance. Our DP index was able to rank the classifier combinations according to their voting performance and thus to suggest the optimal combination. The proposed index is recommended for individual machine learning users as a preliminary tool to identify which classifiers to combine in order to achieve more accurate classification predictions avoiding computer intensive and time-consuming search
The discovery of new functional oxides using combinatorial techniques and advanced data mining algorithms
Electroceramic materials research is a wide ranging field driven by device applications.
For many years, the demand for new materials was addressed largely
through serial processing and analysis of samples often similar in composition to
those already characterised. The Functional Oxide Discovery project (FOXD) is a
combinatorial materials discovery project combining high-throughput synthesis and
characterisation with advanced data mining to develop novel materials.
Dielectric ceramics are of interest for use in telecommunications equipment; oxygen
ion conductors are examined for use in fuel cell cathodes. Both applications
are subject to ever increasing industry demands and materials designs capable of
meeting the stringent requirements are urgently required.
The London University Search Instrument (LUSI) is a combinatorial robot employed
for materials synthesis. Ceramic samples are produced automatically using
an ink-jet printer which mixes and prints inks onto alumina slides. The slides are
transferred to a furnace for sintering and transported to other locations for analysis.
Production and analysis data are stored in the project database. The database
forms a valuable resource detailing the progress of the project and forming a basis
for data mining.
Materials design is a two stage process. The first stage, forward prediction, is accomplished
using an artificial neural network, a Baconian, inductive technique. In a
second stage, the artificial neural network is inverted using a genetic algorithm. The
artificial neural network prediction, stoichiometry and prediction reliability form objectives
for the genetic algorithm which results in a selection of materials designs.
The full potential of this approach is realised through the manufacture and characterisation
of the materials. The resulting data improves the prediction algorithms,
permitting iterative improvement to the designs and the discovery of completely
new materials
Proceedings of Monterey Workshop 2001 Engineering Automation for Sofware Intensive System Integration
The 2001 Monterey Workshop on Engineering Automation for Software Intensive System Integration was sponsored by the Office of Naval Research, Air Force Office of Scientific Research, Army Research Office and the Defense Advance Research Projects Agency. It is our pleasure to thank the workshop advisory and sponsors for their vision of a principled engineering solution for software and for their many-year tireless effort in supporting a series of workshops to bring everyone together.This workshop is the 8 in a series of International workshops. The workshop was held in Monterey Beach Hotel, Monterey, California during June 18-22, 2001. The general theme of the workshop has been to present and discuss research works that aims at increasing the practical impact of formal methods for software and systems engineering. The particular focus of this workshop was "Engineering Automation for Software Intensive System Integration". Previous workshops have been focused on issues including, "Real-time & Concurrent Systems", "Software Merging and Slicing", "Software Evolution", "Software Architecture", "Requirements Targeting Software" and "Modeling Software System Structures in a fastly moving scenario".Office of Naval ResearchAir Force Office of Scientific Research Army Research OfficeDefense Advanced Research Projects AgencyApproved for public release, distribution unlimite
Reports to the President
A compilation of annual reports for the 1988-1989 academic year, including a report from the President of the Massachusetts Institute of Technology, as well as reports from the academic and administrative units of the Institute. The reports outline the year's goals, accomplishments, honors and awards, and future plans
Decision Support Systems
Decision support systems (DSS) have evolved over the past four decades from theoretical concepts into real world computerized applications. DSS architecture contains three key components: knowledge base, computerized model, and user interface. DSS simulate cognitive decision-making functions of humans based on artificial intelligence methodologies (including expert systems, data mining, machine learning, connectionism, logistical reasoning, etc.) in order to perform decision support functions. The applications of DSS cover many domains, ranging from aviation monitoring, transportation safety, clinical diagnosis, weather forecast, business management to internet search strategy. By combining knowledge bases with inference rules, DSS are able to provide suggestions to end users to improve decisions and outcomes. This book is written as a textbook so that it can be used in formal courses examining decision support systems. It may be used by both undergraduate and graduate students from diverse computer-related fields. It will also be of value to established professionals as a text for self-study or for reference
UMSL Bulletin 2020-2021
The 2020-2021 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1084/thumbnail.jp
UMSL Bulletin 2019-2020
The University Bulletin/Course Catalog 2019-2020 Edition.https://irl.umsl.edu/bulletin/1083/thumbnail.jp
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