Discriminant Rule Research Articles

Systemic and Prefrontal Cortical NMDA Receptor Blockade Differentially Affect Discrimination Learning and Set-Shift Ability in Rats.

The authors examined discrimination rule learning and extradimensional set-shifting ability in rats given systemic or intracranial injections of the N-methyl-D-aspartate (NMDA) receptor antagonist MK801. Pretraining systemic injections of MK801 impaired both the acquisition of the initial discrimination rule (Set 1) and the shift to the 2nd rule (Set 2). Pretraining intramedial prefrontal cortical (mPFC) administration of MK801 did not impair Set 1 acquisition. Intra-mPFC injection of MK801 was previously found to impair Set 2 acquisition. Impaired Set 2 performance was due to increased cognitive perseveration. The data suggest that discrimination learning in naive subjects requires NMDA receptors outside the mPFC, whereas NMDA receptors within the mPFC are selectively involved in the modification of previous knowledge and/or the inhibition of previously learned responses.

Typification of Cider Brandy on the Basis of Cider Used in Its Manufacture

A study of typification of cider brandies on the basis of the origin of the raw material used in their manufacture was conducted using chemometric techniques (principal component analysis, linear discriminant analysis, and Bayesian analysis) together with their composition in volatile compounds, as analyzed by gas chromatography with flame ionization to detect the major volatiles and by mass spectrometric to detect the minor ones. Significant principal components computed by a double cross-validation procedure allowed the structure of the database to be visualized as a function of the raw material, that is, cider made from fresh apple juice versus cider made from apple juice concentrate. Feasible and robust discriminant rules were computed and validated by a cross-validation procedure that allowed the authors to classify fresh and concentrate cider brandies, obtaining classification hits of >92%. The most discriminating variables for typifying cider brandies according to their raw material were 1-butanol and ethyl hexanoate.

Selection bias in plots of microarray or other data that have been sampled from a high-dimensional space

For data that have many more features than observations, finding a low-dimensional representation that accurately reflects known prior groupings is non-trivial. Microarray gene expression data, used to create a ``signature'' or discrimination rule that distinguishes cancer tissues that are classified according to type of cancer, is an important special case. The optimal number of features is suitably determined using cross-validation, in which each of several parts of the data becomes in turn the test set, with the remaining data used for training. At each such division or ``fold'' of the data into a training and test set, both the selection of features and the derivation of the discriminant rule must be repeated. Use of the complete data for prior selection of features can lead to a grossly optimistic assessment of predictive accuracy and, in scatter-plot graphs that show discriminant function scores, to a spurious or exaggerated separation between groups. At each division or fold, a second versus first discriminant axis plot of test scores can be drawn. This paper presents a method for bringing these different plots, which have different choices of features and relate to different coordinate systems, into a single plot in which the configuration of points fairly reflects the accuracy of the discriminant procedure. The methodology is applicable, in principle, to use of any discriminant analysis methodology, or of ordination or multidimensional scaling, for obtaining a low dimensional graphical representation of data.

Risk Assessment and Fracture Discrimination by Ultrasound: The Debate Continues

Risk Assessment and Fracture Discrimination by Ultrasound: The Debate Continues

A Laboratory Comparison of Uniform and Discriminative Price Auctions for Reducing Non-point Source Pollution

<i>Auctions allow regulators to identify land management changes with substantial environmental benefit and low opportunity cost. This paper reports an experiment in which seller subjects compete in sealed-offer auctions to obtain part of a fixed budget allocated by the experimenter-regulator to subsidize pollution abatement. One treatment employs uniform-price auction rules, whereas another treatment employs discriminative price auction rules. We find that most offers in the uniform-price auction are within 2% of cost, whereas most offers in the discriminative price auction are at least 8% greater than cost. Nevertheless, the discriminative-price auction has superior overall market performance.</i> (JEL C91, Q15)

Influence of observations on the misclassification probability in quadratic discriminant analysis

In this paper it is studied how observations in the training sample affect the misclassification probability of a quadratic discriminant rule. An approach based on partial influence functions is followed. It allows to quantify the effect of observations in the training sample on the performance of the associated classification rule. Focus is on the effect of outliers on the misclassification rate, merely than on the estimates of the parameters of the quadratic discriminant rule. The expression for the partial influence function is then used to construct a diagnostic tool for detecting influential observations. Applications on real data sets are provided.

Universally consistent conditionalU-statistics for absolutely regular processes and its applications for hidden Markov models

A general class of conditionalU-statistics was introduced by W. Stute as a generalization of the Nadaraya-Watson estimates of a regression function. It was shown that such statistics are universally consistent. Also, universal consistentcies of the window andk n -nearest neighbor estimators (as two special cases of the conditionalU-statistics) were proved. In this paper, we extend these results from the independent case to dependent case. The result is applied to verify the Bayes risk consistency of the corresponding discrimination rules.

Some theory for Fisher's linear discriminant function, `naive Bayes', and some alternatives when there are many more variables than observations

We show that the `naive Bayes' classifier which assumes independent covariates greatly outperforms the Fisher linear discriminant rule under broad conditions when the number of variables grows faster than the number of observations, in the classical problem of discriminating between two normal populations. We also introduce a class of rules spanning the range between independence and arbitrary dependence. These rules are shown to achieve Bayes consistency for the Gaussian `coloured noise' model and to adapt to a spectrum of convergence rates, which we conjecture to be minimax.

Designing supervised classifiers for multiphase flow data classification

There is a great deal of instances in multiphase reactor engineering where the system's state is labeled using categorical indices to depict class memberships. Remarkable examples encompass the labeling of flow patterns and regime transitions in multiphase porous media flows, the macro and microscale fluidization states in fluid–solid or fluid–fluid–solid reactors, the bubble wake morphologies, the bed initial contraction/expansion in fluidization, etc. The design of general-purpose flow pattern recognizers for multiphase reactors requires implementation of feature selection algorithms and classifier design methods. Feature selection algorithms enable extracting the most informative feature sets to be included in the data-driven inference engine (or classifier). In this work, supervised classifiers were built by modeling the class-conditional probability functions of class memberships. The discriminant functions yielding the best class separation were searched using the following statistical and neural-network-based classifiers: Gaussian (quadratic discrimination rule), linear (normal based), nearest mean class, nearest neighbor, k-nearest neighbor, binary decision tree, radial basis functions and multilayer perceptron neural networks. These approaches were benchmarked using, as an example, the three-class trickle-bed flow-regimes problem (Low interaction regime [LIR], high interaction regime [HIR], transition regime [TR]) for which a comprehensive knowledge-referenced database is available (5061 flow regime observation records). For a given model complexity, the multilayer perceptron (MLP) neural network outperformed the other approaches in terms of accuracy. Enhanced phenomenological consistency and immunity against overfitting of the MLP model were achieved by embedding specific domain prior knowledge. The model captured with success the hierarchical linking nature of the categories during monotonic increase (respectively, decrease) of fluid throughputs (respectively, gas density) from LIR to HIR through TR. Misclassification between the non-adjacent LIR and HIR classes was kept to a minimum and the model yielded cross-validated error of 16% for the 5061 database records.

An optimal discriminant rule in the class of linear and quadratic discriminant functions for large dimension and samples

For the classification problem between two normal populations with a common covariance matrix, we consider a class of discriminant rules based on a general discriminant function $T$. The class includes the one based on Fisher’s linear discriminant function and the likelihood ratio rule. Our main purpose is to derive an optimal discriminant rule by using an asymptotic expansion of misclassification probability when both the dimension and the sample sizes are large. We also derive an asymptotically unbiased estimator of the misclassification probability of $T$ in our class.

Kernel-Based Discriminant Techniques for Educational Placement

This article considers the problem of educational placement. Several discriminant techniques are applied to a data set from a survey project of science ability. A profile vector for each student consists of five science-educational indictors. The students are intended to be placed into three reference groups: advanced, regular, and remedial. Various discriminant techniques, including Fisher’s discriminant analysis and kernel-based nonparametric discriminant analysis, are compared. The evaluation work is based on the leaving-one-out misclassification score. Results from the five school data sets and 500 bootstrap samples reveal that the kernel-based nonparametric approach with bandwidth selected by cross validation performs reasonably well. The authors regard kernel-based nonparametric procedures as desirable competitors to Fisher’s discriminant rule for handling problems of educational placement.

Optimal cluster preserving embedding of nonmetric proximity data

For several major applications of data analysis, objects are often not represented as feature vectors in a vector space, but rather by a matrix gathering pairwise proximities. Such pairwise data often violates metricity and, therefore, cannot be naturally embedded in a vector space. Concerning the problem of unsupervised structure detection or clustering, in this paper, a new embedding method for pairwise data into Euclidean vector spaces is introduced. We show that all clustering methods, which are invariant under additive shifts of the pairwise proximities, can be reformulated as grouping problems in Euclidian spaces. The most prominent property of this constant shift embedding framework is the complete preservation of the cluster structure in the embedding space. Restating pairwise clustering problems in vector spaces has several important consequences, such as the statistical description of the clusters by way of cluster prototypes, the generic extension of the grouping procedure to a discriminative prediction rule, and the applicability of standard preprocessing methods like denoising or dimensionality reduction.

Temperature during Incubation as One Factor Affecting the Distribution of Snake River Fall Chinook Salmon Spawning Areas

AbstractSpawning areas of ocean‐type fall Chinook salmon Oncorhynchus tshawytscha in the Pacific Northwest are largely restricted to relatively warm coastal rivers. Fall Chinook salmon spawners in the Snake River basin clearly favor the main‐stem Snake River and the relatively warm lower reaches of its tributaries. In this note, we describe how mean temperature during incubation affects fry emergence date and influences the distribution of fall Chinook salmon spawning areas in the Snake River basin. Using laboratory results and field data, we estimated fry emergence dates for three brood years of fish in seven areas that are consistently used by spawners and in five areas that are rarely used by spawners. Mean incubation temperature was significantly (P &lt; 0.0001) higher in consistently used areas (5.6 ± 0.1°C) than in rarely used areas (4.5 ± 0.1°C). The mean estimated emergence date for the consistently used areas was 12 May (± 20 d), compared with 26 June (± 20 d) for rarely used areas. A logistic discriminant rule fitted from the mean incubation temperatures of the 12 areas discriminated between consistently and rarely used areas with 97% accuracy. We conclude that temperature is one factor that influences the distribution of fall Chinook salmon spawning areas in the Snake River basin.

Searching for discrimination rules in protease proteolytic cleavage activity using genetic programming with a min-max scoring function

This paper presents an algorithm which is able to extract discriminant rules from oligopeptides for protease proteolytic cleavage activity prediction. The algorithm is developed using genetic programming. Three important components in the algorithm are a min-max scoring function, the reverse Polish notation (RPN) and the use of minimum description length. The min-max scoring function is developed using amino acid similarity matrices for measuring the similarity between an oligopeptide and a rule, which is a complex algebraic equation of amino acids rather than a simple pattern sequence. The Fisher ratio is then calculated on the scoring values using the class label associated with the oligopeptides. The discriminant ability of each rule can therefore be evaluated. The use of RPN makes the evolutionary operations simpler and therefore reduces the computational cost. To prevent overfitting, the concept of minimum description length is used to penalize over-complicated rules. A fitness function is therefore composed of the Fisher ratio and the use of minimum description length for an efficient evolutionary process. In the application to four protease datasets (Trypsin, Factor Xa, Hepatitis C Virus and HIV protease cleavage site prediction), our algorithm is superior to C5, a conventional method for deriving decision trees.

Characterization of Cider Brandy on the Basis of Aging Time

ABSTRACT: To characterize cider brandies on the basis of age, chemometric techniques (principal component analysis, linear discriminant analysis, and Bayesean analysis) together with contents of volatile, furanic, and phenolic compounds were used. Significant principal components computed by a double cross‐validation procedure allowed us to visualize the structure of the database as a function of the aging time to which the cider brandies had been subjected. Feasible and robust discriminant rules were computed and validated by a cross‐validation procedure that allowed suitable classification of fresh and aged brandies, obtaining classification hits &gt;; 90%. The most discriminant variables for characterizing cider brandies according to their aging level were as follows: ethyl caprylate &gt; ethyl isovalerate &gt; 1‐propanol &gt; hexyl acetate.

Wavelet‐based estimation of a discriminant function

AbstractIn this paper, we consider wavelet‐based binary linear classifiers. Both consistency results and implementational issues are addressed. We show that under mild assumptions on the design density wavelet discrimination rules are L2‐consistent. The proposed method is illustrated on synthetic data sets in which the ‘truth’ is known and on an applied discrimination problem from the industrial field. Copyright © 2003 John Wiley &amp; Sons, Ltd.

Cash Balance Plans: Cloud of Uncertainty Continues

Cash balance plans have been under attack because employees have protested that conversions of traditional defined benefit plans into cash balance plans are a form of unlawful age discrimination. The media and governmental response led the Internal Revenue Service in 1999 to stop certifying cash balance plans as qualified retirement plans eligible for favorable tax treatment. On December 10, 2002, the Treasury Department issued proposed regulations that would allow defined benefit plans to be converted into cash balance plans without violating age discrimination rules if certain conditions are satisfied. The rules are strongly opposed by many in Congress and by worker advocate groups. Until the rules are made final, they cannot be relied on and the moratorium on technical advice on the conversion's effect on a plan's qualified status will continue. Retirement plan sponsors, participants and beneficiaries will continue to live under a cloud of uncertainty until the rules are made final.

Biochemical and Structural Studies with Prenyl Diphosphate Analogues Provide Insights into Isoprenoid Recognition by Protein Farnesyl Transferase,

Protein farnesyl transferase (PFTase) catalyzes the reaction between farnesyl diphosphate and a protein substrate to form a thioether-linked prenylated protein. The fact that many prenylated proteins are involved in signaling processes has generated considerable interest in protein prenyl transferases as possible anticancer targets. While considerable progress has been made in understanding how prenyl transferases distinguish between related target proteins, the rules for isoprenoid discrimination by these enzymes are less well understood. To clarify how PFTase discriminates between FPP and larger prenyl diphosphates, we have examined the interactions between the enzyme and several isoprenoid analogues, GGPP, and the farnesylated peptide product using a combination of biochemical and structural methods. Two photoactive isoprenoid analogues were shown to inhibit yeast PFTase with K(I) values as low as 45 nM. Crystallographic analysis of one of these analogues bound to PFTase reveals that the diphosphate moiety and the two isoprene units bind in the same positions occupied by the corresponding atoms in FPP when bound to PFTase. However, the benzophenone group protrudes into the acceptor protein binding site and prevents the binding of the second (protein) substrate. Crystallographic analysis of geranylgeranyl diphosphate bound to PFTase shows that the terminal two isoprene units and diphosphate group of the molecule map to the corresponding atoms in FPP; however, the first and second isoprene units bulge away from the acceptor protein binding site. Comparison of the GGPP binding mode with the binding of the farnesylated peptide product suggests that the bulkier isoprenoid cannot rearrange to convert to product without unfavorable steric interactions with the acceptor protein. Taken together, these data do not support the "molecular ruler hypotheses". Instead, we propose a "second site exclusion model" in which PFTase binds larger isoprenoids in a fashion that prevents the subsequent productive binding of the acceptor protein or its conversion to product.

A Linear Programming Approach to Discriminant Analysis with a Reserved-Judgment Region

A linear-programming model is proposed for deriving discriminant rules that allow allocation of entities to a reserved-judgment region. The size of the reserved-judgment region, which can be controlled by varying parameters within the model, dictates the level of aggressiveness (cautiousness) of allocating (misallocating) entities to groups. Results of simulation experiments for various configurations of normal and contaminated normal three-group populations are reported for a variety of parameter selections. Results of cross-validation experiments using real data sets are also reported. Both the simulation and cross-validation experiments include comparison with other discriminant analysis techniques. The results demonstrate that the proposed model is useful for deriving discriminant rules that reduce the chances of misclassification, while maintaining a reasonable level of correct classification.

Automated extraction of hierarchical decision rules from clinical databases using rough set model

Abstract One of the most important problems on rule induction methods is that they cannot extract rules, which plausibly represent experts' decision processes. On one hand, rule induction methods induce probabilistic rules, the description length of which is too short, compared with the experts' rules. On the other hand, construction of Bayesian networks generates too lengthy rules. In this paper, the characteristics of experts' rules are closely examined and a new approach to extract plausible rules is introduced, which consists of the following three procedures. First, the characterization of decision attributes (given classes) is extracted from databases and the classes are classified into several groups with respect to the characterization. Then, two kinds of sub-rules, characterization rules for each group and discrimination rules for each class in the group are induced. Finally, those two parts are integrated into one rule for each decision attribute. The proposed method was evaluated on a medical database, the experimental results of which show that induced rules correctly represent experts' decision processes.