Belief Network Representation Research Articles

Retracted: Automatic Classification Method of Music Genres Based on Deep Belief Network and Sparse Representation

Retracted: Automatic Classification Method of Music Genres Based on Deep Belief Network and Sparse Representation

Retracted] Automatic Classification Method of Music Genres Based on Deep Belief Network and Sparse Representation

Aiming at the problems of poor classification effect, low accuracy, and long time in the current automatic classification methods of music genres, an automatic classification method of music genres based on deep belief network and sparse representation is proposed. The music signal is preprocessed by framing, pre‐emphasis, and windowing, and the characteristic parameters of the music signal are extracted by Mel frequency cepstrum coefficient analysis. The restricted Boltzmann machine is trained layer by layer to obtain the connection weights between layers of the depth belief network model. According to the output classification, the connection weights in the model are fine‐tuned by using the error back‐propagation algorithm. Based on the deep belief network model after fine‐tuning training, the structure of the music genre classification network model is designed. Combined with the classification algorithm of sparse representation, for the training samples of sparse representation music genre, the sparse solution is obtained by using the minimum norm, the sparse representation of test vector is calculated, the category of training samples is judged, and the automatic classification of music genre is realized. The experimental results show that the music genre automatic classification effect of the proposed method is better, the classification accuracy rate is higher, and the classification time can be effectively shortened.

Bayesian Risk Identification Model (BRIM): A Predictive Model to Reduce Use Error Risk in Medical Device Interface Design

Increasing pressure from both regulatory agencies and the consumer market has expanded the need for medical use error reduction. BRIM integrates user performance with risk management to quantifiably predict human factors issues and illuminate design mitigation strategies during development of medical devices. Upfront analytical modeling permits a significant reduction in required expertise and application of empirical methodologies. BRIM asserts that a common set of performance influencing conditions (PICs) determine how a user will interact with a medical device and that a unique set of resulting human response failures (HRFs) manifest differently depending on the specific product interface design. Probability of HRF occurrence can be derived via a Bayesian Belief Network representation of PICs. By understanding the root causes of why a combination of interface, environment, or contextual influences lead to human error, we can predict how a product will perform with respect to human interaction. And, by ...

Validation of a bayesian belief network representation for posterior probability calculations on national crime victimization survey

This paper presents an effort to induce a Bayesian belief network (BBN) from crime data, namely the national crime victimization survey (NCVS). This BBN defines a joint probability distribution over a set of variables that were employed to record a set of crime incidents, with particular focus on characteristics of the victim. The goals are to generate a BBN to capture how characteristics of crime incidents are related to one another, and to make this information available to domain specialists. The novelty associated with the study reported in this paper lies in the use of a Bayesian network to represent a complex data set to non-experts in a way that facilitates automated analysis. Validation of the BBN's ability to approximate the joint probability distribution over the set of variables entailed in the NCVS data set is accomplished through a variety of sources including mathematical techniques and human experts for appropriate triangulation. Validation results indicate that the BBN induced from the NCVS data set is a good joint probability model for the set of attributes in the domain, and accordingly can serve as an effective query tool.

Validation of a bayesian belief network representation for posterior probability calculations on national crime victimization survey

Validation of a bayesian belief network representation for posterior probability calculations on national crime victimization survey

Risk assessment for Clostridium botulinum: a network approach

The construction and implementation of a mathematical framework for the representation of the hazards that arise from Clostridium botulinum growth, and toxin production, in food are described. Botulism has been recognised as a serious foodborne illness for over a century and, more recently, has become the subject of increased concern due to changing processing and consumption patterns associated with foods. In this respect quantitative risk assessment has an increasingly important role to play in assisting risk management and ensuring the safety of minimally processed foods and foods with extended shelf life. Bayesian Belief Networks are a type of expert system that integrates a graphical, flow diagram like, representation of a hazard domain with a powerful technique for combining probabilities. This technique facilitates the accumulation of understanding and experience, for particular hazard domains, into computer tools that can be used to inspect risks and account for decisions. Analysis of the hazards associated with foodborne botulism involves Belief Network components that represent contamination processes, thermal death kinetics for spores, germination and growth of cells, toxin production and patterns of consumer behaviour, etc. These developments are discussed and three important aspects of the food safety information supply, complexity, dependency and uncertainty highlighted. The benefits associated with a Bayesian view of food safety assessment are illustrated by a Belief Network representation which supports, and prioritises, decisions and actions that (a) minimise the chances and extent of detrimental events and (b) maximise opportunities for awareness and control.

Decision Support Under Incomplete and Uncertain Information

The present paper deals with problems of influence diagrams construction and its solution for decision support systems that function under uncertain and incomplete information available. The scheme of goal-directed generation of influence diagrams is considered. A method for using belief network algorithms to solve influence diagram problems is proposed This method is based on the transformation of an influence diagram into a belief network. An algorithm for hierarchical probabilistic inference is presented that is based on belief network representation in the form of a dependence tree according to which probabilities are determined in the order of their increase in the dependence tree level hierarchy. The suggested technique of influence diagram construction and analysis has been used for the task of logistic transport chain development.

Constructing efficient belief network structures with expert provided information

Presents a technique to construct efficient belief network structures for application areas where large amounts of data are available and information on the ordering of the variables can be obtained from domain experts. We identify classes of networks that are efficient for propagating beliefs. We formulate the problem as one of determining the belief network representation from a given class that best represents the data. We use the I-Divergence measure which is known to have certain desirable properties for evaluating different approximations. We present some theoretical findings that characterize the nature of solutions that are obtained. These theoretical results lead to an efficient solution procedure for finding the best network representation. We also discuss other information that may be reasonably obtained from experts, and show how such information leads to improving the efficiency of the technique to find the best network structure.

Time series prediction using belief network models

We address the problem of generating normative forecasts efficiently from a Bayesian belief network. Forecasts are predictions of future values of domain variables conditioned on current and past values of domain variables. To address the forecasting problem, we have developed a probability forecasting methodology, Dynamic Network Models (DNMs), through a synthesis of belief network models and classical time-series models. The DNM methodology is based on the integration of fundamental methods of Bayesian time-series analysis, with recent additive generalizations of belief network representation and inference techniques. We apply DNMs to the problem of forecasting episodes of apnea, that is, regular intervals of breathing cessation in patients afflicted with sleep apnea. We compare the one-step-ahead forecasts of chest volume, an indicator of apnea, made by autoregressive models, belief networks, and DNMs. We also construct a DNM to analyse the multivariate time series of chest volume, heart rate and oxygen saturation data.

From certainty factors to belief networks

The certainty-factor (CF) model is a commonly used method for managing uncertainty in rule-based systems. We review the history and mechanics of the CF model, and delineate precisely its theoretical and practical limitations. In addition, we examine the belief network, a representation that is similar to the CF model but that is grounded firmly in probability theory. We show that the belief-network representation overcomes many of the limitations of the CF model, and provides a promising approach to the practical construction of expert systems.

Toward Normative Expert Systems: Part II Probability-Based Representations for Efficient Knowledge Acquisition and Inference

We address practical issues concerning the construction and use of decision-theoretic or normative expert systems for diagnosis. In particular, we examine Pathfinder, a normative expert system that assists surgical pathologists with the diagnosis of lymph-node diseases, and discuss the representation of dependencies among pieces of evidence within this system. We describe the belief network, a graphical representation of probabilistic dependencies. We see how Pathfinder uses a belief network to construct differential diagnosis efficiently, even when there are dependencies among pieces of evidence. In addition, we introduce an extension of the belief-network representation called a similarity network, a tool for constructing large and complex belief networks. The representation allows a user to construct independent belief networks for subsets of a given domain. A valid belief network for the entire domain can then be constructed from the individual belief networks. We also introduce the partition, a graphical representation that facilitates the assessment of probabilities associated with a belief network. We show that the similarity-network and partition representations made practical the construction of Pathfinder.

Probabilistic Diagnosis Using a Reformulation of the INTERNIST-1/QMR Knowledge Base

We have developed a probabilistic reformulation of the Quick Medical Reference (QMR) system. In Part I of this two-part series, we described a two-level, multiply connected belief-network representation of the QMR knowledge base and a simulation algorithm to perform probabilistic inference on the reformulated knowledge base. In Part II of this series, we report on an evaluation of the probabilistic QMR, in which we compare the performance of QMR to that of our probabilistic system on cases abstracted from continuing medical education materials from Scientific American Medicine. In addition, we analyze empirically several components of the probabilistic model and simulation algorithm.

Probabilistic Diagnosis Using a Reformulation of the INTERNIST-1/QMR Knowledge Base

In Part I of this two-part series, we report the design of a probabilistic reformulation of the Quick Medical Reference (QMR) diagnostic decision-support tool. We describe a two-level multiply connected belief-network representation of the QMR knowledge base of internal medicine. In the belief-network representation of the QMR knowledge base, we use probabilities derived from the QMR disease profiles, from QMR imports of findings, and from National Center for Health Statistics hospital-discharge statistics. We use a stochastic simulation algorithm for inference on the belief network. This algorithm computes estimates of the posterior marginal probabilities of diseases given a set of findings. In Part II of the series, we compare the performance of QMR to that of our probabilistic system on cases abstracted from continuing medical education materials from Scientific American Medicine. In addition, we analyze empirically several components of the probabilistic model and simulation algorithm.