Discrimination Information Measure Research Articles

On Past Extropy and Negative Cumulative Extropy Properties of Ranked Set Sampling and Maximum Ranked Set Sampling with Unequal Samples

Ranked set sampling is considered as an alternative to simple random sampling and maximum ranked set sampling is a very useful modification of ranked set sampling. In this paper we focused on information content of ranked set sampling and maximum ranked set sampling with unequal samples in terms of past extropy measure and also considered the information content of negative cumulative extropy and its dynamic version based on maximum ranked set sampling and simple random sampling designs. We also compare ranked set sampling data, maximum ranked set sampling data with simple random sampling and with each other. Also here we obtained a new discrimination information measure among simple random sampling data, ranked set sampling data and maximum ranked set sampling data for past extropy measure.

Document Clustering Using Graph Based Fuzzy Association Rule Generation

With the wider growth of web-based documents, the necessity of automatic document clustering and text summarization is increased. Here, document summarization that is extracting the essential task with appropriate information, removal of unnecessary data and providing the data in a cohesive and coherent manner is determined to be a most confronting task. In this research, a novel intelligent model for document clustering is designed with graph model and Fuzzy based association rule generation (gFAR). Initially, the graph model is used to map the relationship among the data (multi-source) followed by the establishment of document clustering with the generation of association rule using the fuzzy concept. This method shows benefit in redundancy elimination by mapping the relevant document using graph model and reduces the time consumption and improves the accuracy using the association rule generation with fuzzy. This framework is provided in an interpretable way for document clustering. It iteratively reduces the error rate during relationship mapping among the data (clusters) with the assistance of weighted document content. Also, this model represents the significance of data features with class discrimination. It is also helpful in measuring the significance of the features during the data clustering process. The simulation is done with MATLAB 2016b environment and evaluated with the empirical standards like Relative Risk Patterns (RRP), ROUGE score, and Discrimination Information Measure (DMI) respectively. Here, DailyMail and DUC 2004 dataset is used to extract the empirical results. The proposed gFAR model gives better trade-off while compared with various prevailing approaches.

Boosting Discrimination Information Based Document Clustering Using Consensus and Classification

Adequate choice of term discrimination information measure (DIM) stipulates guaranteed document clustering. Exercise for the right choice is empirical in nature, and characteristics of data in the documents help experts to speculate a viable solution. Thus, a consistent DIM for the clustering is a mere conjecture and demands intelligent selection of the information measure. In this work, we propose an automated consensus building measure based on a text classifier. Two distinct DIMs construct basic partitions of documents and form base clusters. The consensus building measure method uses the clusters information to find concordant documents and constitute a dataset to train the text classifier. The classifier predicts labels for discordant documents from earlier clustering stage and forms new clusters. The experimentation is performed with eight standard data sets to test efficacy of the proposed technique. The improvement observed by applying the proposed consensus clustering demonstrates its superiority over individual results. Relative Risk (RR) and Measurement of Discrimination Information (MDI) are the two discrimination information measures used for obtaining the base clustering solutions in our experiments.

Characterizations of bivariate models using dynamic Kullback–Leibler discrimination measures

In this paper, the residual Kullback–Leibler discrimination information measure is extended to conditionally specified models. The extension is used to characterize some bivariate distributions. These distributions are also characterized in terms of proportional hazard rate models and weighted distributions. Moreover, we also obtain some bounds for this dynamic discrimination function by using the likelihood ratio order and some preceding results.

Intuitionistic fuzzy information – Applications to pattern recognition

This paper addresses the issue of information-theoretic discrimination measures for intuitionistic fuzzy sets (IFSs). Although many measures of distance, similarity, dissimilarity, and correlation between IFSs have been proposed, there is no reference regarding information-driven measures used for comparison between sets. In this work we introduce the concepts of discrimination information and cross-entropy in the intuitionistic fuzzy setting and we derive an extension of the De Luca–Termini nonprobabilistic entropy for IFSs. Based on this entropy, we reveal an intuitive and mathematical connection between the notions of entropy for fuzzy sets (FSs) and IFSs in terms of fuzziness and intuitionism. Finally, we demonstrate the efficiency of the proposed discrimination information measure for pattern recognition, medical diagnosis, and image segmentation.

Frequency analysis and characterization in orthogonal cutting of glass fiber reinforced composites

Abstract This study discusses frequency analysis based on frequency spectrum and autoregressive (AR) time series model, and process characterization in orthogonal cutting of fiber–matrix composite materials. A sparsely distributed idealized composite material, namely a glass fiber reinforced polyester (GFRP) was used as the workpiece. The analysis method employs a force sensor and the signals from the sensor are processed using either the fast Fourier transform (FFT) technique or AR time series model. Signal distortion measure based on discrimination information is also introduced. The experimental correlations between the different chip formation mechanisms and power spectrum or AR model coefficients are then established. In particular, only those features that are most sensitive to different types of cutting mechanisms are selected by feature extraction method in AR modeling. Selected features are used to characterize the chip formation. Discrimination information measure proves to be useful in signal analysis when any characteristic of the cutting process is apparent in the form of spectral peaks. Effects of fiber orientation, cutting parameters and tool geometry on the cutting mechanisms and surface quality are also discussed.

Optimal Auxiliary Input Design for Fault Detection of Systems with Model Uncertainty

Introduction of an auxiliary input is known to be useful to detect the system fault quickly without affecting the original system behavior in normal mode. Such an auxiliary input is designed to enlarge the distance measured by the Kullback discrimination information measure between the system models corresponding to the normal and the fault modes. However, in practice, the designer hardly knows the exact models. Hence, the optimal auxiliary input should be designed for the case of the system models with uncertainty. Here, the auxiliary input is designed to maximize the distance for the worst combination of system models. Numerical simulation results indicate the applicability of the proposed auxiliary input in fault detection

Optimal auxiliary input design for fault diagnosis

An optimal input design problem in frequency domain is discussed for quick fault diagnosis of dynamical systems without affecting the original system in normal mode. Since the optimal auxiliary input for detecting a certain fault mode is not always good for detection of other fault modes, i.e., it may reduce the the distance between the normal mode and the other fault modes and make harder to detect true fault mode, the max-min approach is proposed in this paper. The auxiliary input is designed to maximize the minimum distance measured by the Kullback discrimination information measure between system models corresponding to the normal and each fault mode in order to detect the true fault mode. Results of a numerical simulation result indicate the applicability of the proposed auxiliary input in fault diagnosis.

Auditory distortion measure for speech coder evaluation — Discrimination information approach

In this article, we devise a fidelity criterion for quantifying the degree of distortion introduced by a speech coder. An original speech and its coded version are transformed from the time-domain to a perceptual-domain using an auditory (cochlear) model. This perceptual-domain representation provides information pertaining to the probability-of-firings in the neural channels. The introduced cochlear discrimination information (CDI) measure compares these firing probabilities in an information-theoretic sense. In essence, it evaluates the cross-entropy of the neural firings for the coded speech with respect to those for the original one. The performance of this objective measure is compared with subjective evaluation results. Finally, we provide a rate-distortion analysis by computing the rate-distortion function for speech coding using the Blahut algorithm. Four state-of-the-art speech coders with rates ranging from 4.8 kbit/s (CELP) to 32 kbit/s (ADPCM) are studied from the view-point of their performances (as assessed by the CDI measure) with respect to the rate-distortion limits.

A simple Duality Proof for Quadratically Constrained Entropy Functionals and Extension to Convex Constraints

In this paper, a simple proof of duality results is presented that was recently derived by Zhang and Brockett [SIAM J. Appl. Math., 47 (1987), pp. 871–885] for the problem of minimizing the Kullback–Liebler discrimination information measure subject to quadratic and linear inequality constraints. The proof is a direct and simple application of Lagrangian Duality Theory and is extended to a more general class of entropy optimization problems with composite convex constraints.

A minimum discrimination information approach for hidden Markov modeling

An iterative approach for minimum-discrimination-information (MDI) hidden Markov modeling of information sources is proposed. The approach is developed for sources characterized by a given set of partial covariance matrices and for hidden Markov models (HMMs) with Gaussian autoregressive output probability distributions (PDs). The approach aims at estimating the HMM which yields the MDI with respect to all sources that could have produced the given set of partial covariance matrices. Each iteration of the MDI algorithm generates a new HMM as follows. First, a PD for the source is estimated by minimizing the discrimination information measure with respect to the old model over all PDs which satisfy the given set of partial covariance matrices. Then a new model that decreases the discrimination information measure between the estimated PD of the source and the PD of the old model is developed. The problem of estimating the PD of the source is formulated as a standard constrained minimization problem in the Euclidean space. The estimation of a new model given the PD of the source is done by a procedure that generalizes the Baum algorithm. The MDI approach is shown to be a descent algorithm for the discrimination information measure, and its local convergence is proved. >

Asymptotic minimum discrimination information measure for asymptotically weakly stationary processes

An explicit expression is derived for the minimum discrimination information (MDI) measure with respect to Gaussian priors for sources characterized by their mean and by any principal leading block of their covariance matrix. An explicit expression is provided for the MDI extension of the given partial covariance of the source with respect to a Gaussian prior. For zero-mean sources and zero-mean Gaussian priors that are asymptotically weakly stationary (AWS) processes, it is shown that the asymptotic MDI measure equals half the Itakura-Saito distortion measure between the asymptotic power spectral densities of the source and prior. Asymptotic MDI modelling of a given AWS source by autoregressive and autoregressive moving average models, which are AWS models, is considered, and conditions are given for convergence of the sample covariance estimator of the source to the stationary covariance used in the modelling.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Optimal Input Design for Discrimination of Linear Stochastic Models Based on Kullback–Leibler Discrimination Information Measure

Optimal input design for stochastic dynamic system identification has attracted considerable attention to obtain maximal information from the observation data. Most studies in this area have been devoted to accurate parameter estimation within a specified model structure. However, the problem of primary importance in system identification might be to determine the model structure itself. From this point of view, we have considered the optimal input design problem for autoregressive model discrimination based on the Kullback-Leibier discrimination information (KLDI) measure (Uosaki and Hatanaka (1987)). In this paper, we derive an optimal input based on the KLDI measure which discriminates efficiently more general linear stochastic models including autoregressive models. Several digital simulation experiments exemplify that the proposed input works well in general linear stochastic model discrimination compared to other inputs such as random input.

Optimal Input Design for Autoregressive Model Discrimination Based on the Kullback-Leibler Discrimination Information

Experiment design for dynamic system identification has attracted considerable attention to obtain the maximal information from the observed input/output data. Most studies in this area have been devoted to accurate parameter estimation within a specified model structure. However, the problem of primary importance in system identification might be to determine the model structure itself. From this point of view, the optimal input design problem is discussed in this paper for discriminating two rival autoregressive models efficiently. An optimal input is derived, which maximizes the time increment of the Kullback-Leibler discrimination information measure to make the distance between two models as big as possible. It is exemplified the applicability of the proposed input for efficient autoregressive model discrimination by simulation studies.

An information theory model for the evaluation of circumstantial evidence

The problem of assessing the weight of circumstantial evidence linking a suspect with the perpetrator of a crime is considered. The quantitative measure PG of weight of circumstantial evidence that is used is the probability that culprit and suspect may be identified with certainty as one and the same based solely upon the circumstantial evidence. A unifying model for evaluating PG taking into account expert testimony is established on the basis of the Kullback-Leibler discrimination information measure. This model is applied to deriving PG based upon a jurist's interpretation of the expert testimony. An important property of the model is the first-order agreement, as the testimony becomes compelling, of PGs based on different jurists' viewpoints. The methodology developed is applied to the well-known Collins case.

An application of the discrimination information measure to the theory of testing hypotheses Part I

An application of the discrimination information measure to the theory of testing hypotheses Part I

An application of the discrimination information measure to the theory of testing hypotheses Part II

An application of the discrimination information measure to the theory of testing hypotheses Part II