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PubMed1999

A fuzzy logic-controlled classifier for use in implantable cardioverter defibrillators.

PURPOSE: Implantable cardioverters defibrillators (ICDs) are increasingly used in the management of life-threatening arrhythmias. Correct recognition of a treatable arrhythmia is crucial to this application. However, the computational power of microprocessors currently used in ICDs limits the range of traditional algorithms available for this application. METHODS: Classification based on fuzzy inference systems (FIS) were trained to recognize different cardiac rhythms (AF, VF, SVT, VT) from the Ann Arbor Electrogram Library. The FIS used were designed using adaptive-network-based fuzzy inference methods to optimize the classification procedure. Only computational techniques suitable for ICD design were used. RESULTS: After pretraining with the ANFIS correct rhythm classification was observed for the rhythms studied. CONCLUSION: In this preliminary study, successful rhythm classification was demonstrated using fuzzy logic techniques. In view of the computational efficiency this may have application in ICD design.

Arrhythmias, Cardiac↗
PubMed2001

Automated seed detection and three-dimensional reconstruction. I. Seed localization from fluoroscopic images or radiographs.

An automated procedure for the detection of the position and the orientation of radioactive seeds on fluoroscopic images or scanned radiographs is presented. The extracted positions of seed centers and the orientations are used for three-dimensional reconstruction of permanent prostate implants. The extraction procedure requires several steps: correction of image intensifier distortions, normalization, background removal, automatic threshold selection, thresholding, and finally, moment analysis and classification of the connected components. The algorithm was tested on 75 fluoroscopic images. The results show that, on average, 92% of the seeds are detected automatically. The orientation is found with an error smaller than 50 for 75% of the seeds. The orientation of overlapping seeds (10%) should be considered as an estimate at best. The image processing procedure can also be used for seed or catheter detection in CT images, with minor modifications.

Algorithms↗
PubMed2000

Convergence analysis of cascade error projection--an efficient learning algorithm for hardware implementation.

In this paper, we present a mathematical foundation, including a convergence analysis, for cascading architecture neural network. Our analysis also shows that the convergence of the cascade architecture neural network is assured because it satisfies Liapunov criteria, in an added hidden unit domain rather than in the time domain. From this analysis, a mathematical foundation for the cascade correlation learning algorithm can be found. Furthermore, it becomes apparent that the cascade correlation scheme is a special case from mathematical analysis in which an efficient hardware learning algorithm called Cascade Error Projection(CEP) is proposed. The CEP provides efficient learning in hardware and it is faster to train, because part of the weights are deterministically obtained, and the learning of the remaining weights from the inputs to the hidden unit is performed as a single-layer perceptron learning with previously determined weights kept frozen. In addition, one can start out with zero weight values (rather than random finite weight values) when the learning of each layer is commenced. Further, unlike cascade correlation algorithm (where a pool of candidate hidden units is added), only a single hidden unit is added at a time. Therefore, the simplicity in hardware implementation is also achieved. Finally, 5- to 8-bit parity and chaotic time series prediction problems are investigated; the simulation results demonstrate that 4-bit or more weight quantization is sufficient for learning neural network using CEP. In addition, it is demonstrated that this technique is able to compensate for less bit weight resolution by incorporating additional hidden units. However, generation result may suffer somewhat with lower bit weight quantization.

Algorithms↗
PubMed2005

The combined use of self-organizing maps and Andrews' Curves.

The use of self-organizing maps to analyze data often depends on finding effective methods to visualize the SOM's structure. In this paper we propose a new way to perform that visualization using a variant of Andrews' Curves. Also we show that the interaction between these two methods allows us to find sub-clusters within identified clusters. Perhaps more importantly, using the SOM to pre-process data by identifying gross features enables us to use Andrews' Curves on data sets which would have previously been too large for the methodology. Finally we show how a three way interaction between the human user and these two methods can be a valuable exploratory data analysis tool.

Algorithms↗
PubMed1996

Concept hierarchy memory model: a neural architecture for conceptual knowledge representation, learning, and commonsense reasoning.

This article introduces a neural network based cognitive architecture termed Concept Hierarchy Memory Model (CHMM) for conceptual knowledge representation and commonsense reasoning. CHMM is composed of two subnetworks: a Concept Formation Network (CFN), that acquires concepts based on their sensory representations; and a Concept Hierarchy Network (CHN), that encodes hierarchical relationships between concepts. Based on Adaptive Resonance Associative Map (ARAM), a supervised Adaptive Resonance Theory (ART) model, CHMM provides a systematic treatment for concept formation and organization of a concept hierarchy. Specifically, a concept can be learned by sampling activities across multiple sensory fields. By chunking relations between concepts as cognitive codes, a concept hierarchy can be learned/modified through experience. Also, fuzzy relations between concepts can now be represented in terms of the weights on the links connecting them. Using a unified inferencing mechanism based on code firing, CHMM performs an important class of commonsense reasoning, including concept recognition and property inheritance.

Algorithms↗
PubMed2001

Local reinforcement and recombination in classifier systems.

We investigate classifier systems' reward schemes by way of an example that highlights the interaction of local reward schemes and recombination. We contrast averaging schemes and maximizing schemes. Our example illustrates a sense in which certain recombination operators mesh more gracefully with averaging schemes than with maximizing schemes.

Algorithms↗
PubMed2003

Analysis and improvement of fitness exploitation in XCS: bounding models, tournament selection, and bilateral accuracy.

The evolutionary learning mechanism in XCS strongly depends on its accuracy-based fitness approach. The approach is meant to result in an evolutionary drive from classifiers of low accuracy to those of high accuracy. Since, given inaccuracy, lower specificity often corresponds to lower accuracy, fitness pressure most often also results in a pressure towards higher specificity. Moreover, fitness pressure should cause the evolutionary process to be innovative in that it combines low-order building blocks of lower accurate classifiers, to higher-order building blocks with higher accuracy. This paper investigates how, when, and where accuracy-based fitness results in successful rule evolution in XCS. Along the way, a weakness in the current proportionate selection method in XCS is identified. Several problem bounds are derived that need to be obeyed to enable proper evolutionary pressure. Moreover, a fitness dilemma is identified that causes accuracy-based fitness to be misleading. Improvements are introduced to XCS to make fitness pressure more robust and overcome the fitness dilemma. Specifically, (1) tournament selection results in a much better fitness-bias exploitation, and (2) bilateral accuracy prevents the fitness dilemma. While the improvements stand for themselves, we believe they also contribute to the ultimate goal of an evolutionary learning system that is able to solve decomposable machine-learning problems quickly, accurately,and reliably. The paper also contributes to the further understanding of XCS in general and the fitness approach in XCS in particular.

Algorithms↗
PubMed2003

Bounding the effect of noise in multiobjective learning classifier systems.

This paper analyzes the impact of using noisy data sets in Pittsburgh-style learning classifier systems. This study was done using a particular kind of learning classifier system based on multiobjective selection. Our goal was to characterize the behavior of this kind of algorithms when dealing with noisy domains. For this reason, we developed a theoretical model for predicting the minimal achievable error in noisy domains. Combining this theoretical model for crisp learners with graphical representations of the evolved hypotheses through multiobjective techniques, we are able to bound the behavior of a learning classifier system. This kind of modeling lets us identify relevant characteristics of the evolved hypotheses, such as overfitting conditions that lead to hypotheses that poorly generalize the concept to be learned.

Algorithms↗
PubMed2005

RGFGA: an efficient representation and crossover for grouping genetic algorithms.

There is substantial research into genetic algorithms that are used to group large numbers of objects into mutually exclusive subsets based upon some fitness function. However, nearly all methods involve degeneracy to some degree. We introduce a new representation for grouping genetic algorithms, the restricted growth function genetic algorithm, that effectively removes all degeneracy, resulting in a more efficient search. A new crossover operator is also described that exploits a measure of similarity between chromosomes in a population. Using several synthetic datasets, we compare the performance of our representation and crossover with another well known state-of-the-art GA method, a strawman optimisation method and a well-established statistical clustering algorithm, with encouraging results.

Algorithms↗
PubMed2004

Rule fitness and pathology in learning classifier systems.

It has long been known that in some relatively simple reinforcement learning tasks traditional strength-based classifier systems will adapt poorly and show poor generalisation. In contrast, the more recent accuracy-based XCS, appears both to adapt and generalise well. In this work, we attribute the difference to what we call strong over general and fit over general rules. We begin by developing a taxonomy of rule types and considering the conditions under which they may occur. In order to do so an extreme simplification of the classifier system is made, which forces us toward qualitative rather than quantitative analysis. We begin with the basics, considering definitions for correct and incorrect actions, and then correct, incorrect, and overgeneral rules for both strength and accuracy-based fitness. The concept of strong overgeneral rules, which we claim are the Achilles' heel of strength-based classifier systems, are then analysed. It is shown that strong overgenerals depend on what we call biases in the reward function (or, in sequential tasks, the value function). We distinguish between strong and fit overgeneral rules, and show that although strong overgenerals are fit in a strength-based system called SB-XCS, they are not in XCS. Next we show how to design fit overgeneral rules for XCS (but not SB-XCS), by introducing biases in the variance of the reward function, and thus that each system has its own weakness. Finally, we give some consideration to the prevalence of reward and variance function bias, and note that non-trivial sequential tasks have highly biased value functions.

Algorithms↗
PubMed2006

Enhancing text categorization with semantic-enriched representation and training data augmentation.

OBJECTIVE: Acquiring and representing biomedical knowledge is an increasingly important component of contemporary bioinformatics. A critical step of the process is to identify and retrieve relevant documents among the vast volume of modern biomedical literature efficiently. In the real world, many information retrieval tasks are difficult because of high data dimensionality and the lack of annotated examples to train a retrieval algorithm. Under such a scenario, the performance of information retrieval algorithms is often unsatisfactory, therefore improvements are needed. DESIGN: We studied two approaches that enhance the text categorization performance on sparse and high data dimensionality: (1) semantic-preserving dimension reduction by representing text with semantic-enriched features; and (2) augmenting training data with semi-supervised learning. A probabilistic topic model was applied to extract major semantic topics from a corpus of text of interest. The representation of documents was projected from the high-dimensional vocabulary space onto a semantic topic space with reduced dimensionality. A semi-supervised learning algorithm based on graph theory was applied to identify potential positive training cases, which were further used to augment training data. The effects of data transformation and augmentation on text categorization by support vector machine (SVM) were evaluated. RESULTS AND CONCLUSION: Semantic-enriched data transformation and the pseudo-positive-cases augmented training data enhance the efficiency and performance of text categorization by SVM.

Algorithms↗
PubMed2000

Issues of threshold selection when determining the fractal dimension in HRCT slices of lumbar vertebrae.

The box counting dimension is a frequently applied tool for the classification of trabecular bone structure. The algorithm requires a binarization of the gray value data, for example that acquired by high resolution CT (HRCT). We recently proposed a method to eliminate bone mineral density (BMD) by applying a linear normalization scheme. Further consideration has shown that full BMD independence has not been achieved, and the structural parameter proposed was therefore difficult to interpret. In this study we present an alternative approach to obtain a structural parameter that is independent of BMD. HRCT volume data was acquired on 21 lumbar vertebrae from five cadavers. In the segmented spongiosa, thresholding was based on different quantiles of the gray value histogram, yielding invariance over linear and non-linear transformations. Thresholding at high gray value levels (80% quantile) shows the highest level of significance when discriminating between osteoporotic and non-osteoporotic cases. As an addition to the measurement of BMD alone, the determination of structural properties allows an improvement of the assessment of the individual fracture risk.

Algorithms↗
PubMed2000

The relationship between heavy alcohol use and work productivity loss in active duty military personnel: a secondary analysis of the 1995 Department of Defense Worldwide Survey.

This cross-sectional study examines the association between heavy alcohol use among active duty military personnel and five work productivity loss events that may have an adverse effect on military performance and readiness. Data for light (N = 3,147) and heavy (N = 2,242) drinkers, categorized by gender and pay grade, were obtained from the 1995 Department of Defense Worldwide Survey. Drinking classification was predefined using a standard algorithm that factored quantity and frequency of wine, beer, and liquor consumed. The relative risks of experiencing a productivity loss event at a particular level and 95% confidence intervals were calculated by applying the Mantel-Haenszel method after adjusting for age. The relative risks for increased self-reported lateness, leaving early, low performance, and on-the-job injury were all higher for heavy drinkers than for light drinkers. This association between the heavy-drinking population and four of five work productivity loss events indicates that prevention programs should target all personnel.

Absenteeism↗
PubMed2003

Novel versus unsupported clades: assessing the qualitative support for clades in MRP supertrees.

Matrix representation with parsimony (MRP) supertree construction has been criticized because the supertree may specify clades that are contradicted by every source tree contributing to it. Such unsupported clades may also occur using other supertree methods; however, their incidence is largely unknown. In this study, I investigated the frequency of unsupported clades in both simulated and empirical MRP supertrees. Here, I propose a new index, QS, to quantify the qualitative support for a supertree and its clades among the set of source trees. Results show that unsupported clades are very rare in MRP supertrees, occurring most often when there are few source trees that all possess the same set of taxa. However, even under these conditions the frequency of unsupported clades was <0.2%. Unsupported clades were absent from both the Carnivora and Lagomorpha supertrees, reflecting the use of large numbers of source trees for both. The proposed QS indices are correlated broadly with another measure of quantitative clade support (bootstrap frequencies, as derived from resampling of the MRP matrix) but appear to be more sensitive. More importantly, they sample at the level of the source trees and thus, unlike the bootstrap, are suitable for summarizing the support of MRP supertree clades.

Algorithms↗
PubMed2003

Derivation of malignancy status from ICD-9 codes.

To assess the severity of illness of oncology patients, it is necessary to distinguish patients with a single primary tumor from patients with metastatic disease occurring at a secondary location remote from the primary site. We developed a ranked list of cancer groupings and an algorithm that could distinguish patients with primary and metastatic cancer even if no specific code for secondary cancer was recorded. In patients with metastatic disease, the algorithm should also distinguish the primary site from the secondary site.

Algorithms↗
PubMed2004

Acquiring meaning for French medical terminology: contribution of morphosemantics.

Morphologically complex words, and particularly neoclassical compounds, form more than 60% of the neologisms in the biomedical field. Guessing their definitions and grouping them into semantic classes by means of lexical relations are thus two crucial improvements for handling these words, e.g., for information retrieval, indexing and text understanding applications. This paper describes a morphosemantic linguistic-based parser called DériF, currently developed in the framework of two projects, UMLF and VUMeF, and its application to French biomedical derived and compound words. It shows how the resulting morphologically tagged lexicon is enriched by semantic relations leading both to the synthesis of pseudo-definitions and to the constitution of classes of synonyms, hypo- and hypernyms.

Algorithms↗