Showing papers on "Linear discriminant analysis published in 1994"

Applied discriminant analysis

Abstract: Partial table of contents: Discriminant Analysis in Research. PREDICTION. Basic Ideas of Classification. Multivariate Normal Rules. Classification Results. Hit Rate Estimation. Nonnormal Rules. Reporting Results of a PDA. Applications of PDA. DESCRIPTION. Group Separation. Assessing Effects. Describing Effects. Selecting and Ordering Variables. Reporting Results of a DDA. Applications of DDA. ISSUES AND PROBLEMS. Special Problems. Appendices. Answers to Exercises. References. Index.

Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)

Abstract: This study analyzes the comparison between traditional statistical methodologies for distress classification and prediction, i.e., linear discriminant (LDA) or logit analyses, with an artificial intelligence algorithm known as neural networks (NN). Analyzing well over 1,000 healthy, vulnerable and unsound industrial Italian firms from 1982–1992, this study was carried out at the Centrale dei Bilanci in Turin, Italy and is now being tested in actual diagnostic situations. The results are part of a larger effort involving separate models for industrial, retailing/trading and construction firms. The results indicate a balanced degree of accuracy and other beneficial characteristics between LDA and NN. We are particularly careful to point out the problems of the ‘black-box’ NN systems, including illogical weightings of the indicators and overfitting in the training stage both of which negatively impacts predictive accuracy. Both types of diagnoslic techniques displayed acceptable, over 90%, classificalion and holdoul sample accuracy and the study concludes that there certainly should be further studies and tests using the two lechniques and suggests a combined approach for predictive reinforcement.

Flexible Discriminant Analysis by Optimal Scoring

Abstract: Fisher's linear discriminant analysis is a valuable tool for multigroup classification. With a large number of predictors, one can find a reduced number of discriminant coordinate functions that are “optimal” for separating the groups. With two such functions, one can produce a classification map that partitions the reduced space into regions that are identified with group membership, and the decision boundaries are linear. This article is about richer nonlinear classification schemes. Linear discriminant analysis is equivalent to multiresponse linear regression using optimal scorings to represent the groups. In this paper, we obtain nonparametric versions of discriminant analysis by replacing linear regression by any nonparametric regression method. In this way, any multiresponse regression technique (such as MARS or neural networks) can be postprocessed to improve its classification performance.

Bankruptcy prediction using neural networks

Abstract: Prediction of firm bankruptcies have been extensively studied in accounting, as all stakeholders in a firm have a vested interest in monitoring its financial performance. This paper presents an exploratory study which compares the predictive capabilities for firm bankruptcy of neural networks and classical multivariate discriminant analysis. The predictive accuracy of the two techniques is presented within a comprehensive, statistically sound framework, indicating the value added to the forecasting problem by each technique. The study indicates that neural networks perform significantly better than discriminant analysis at predicting firm bankruptcies. Implications of our results for the accounting professional, neural networks researcher and decision support system builders are highlighted.

Transform features for texture classification and discrimination in large image databases

Abstract: Proposes a method for classification and discrimination of textures based on the energies of image subbands. The authors show that with this relatively simple feature set, effective texture discrimination can be achieved. In the paper, subband-energy feature sets extracted from the following typical image decompositions are compared: wavelet subband, uniform subband, discrete cosine transform (DCT), and spatial partitioning. The authors report that over 90% correct classification was attained using the feature set in classifying the full Brodatz [1965] collection of 112 textures. Furthermore, the subband energy-based feature set can be readily applied to a system for indexing images by texture content in image databases, since the features can be extracted directly from spatial-frequency decomposed image data. The authors also show that to construct a suitable space for discrimination, Fisher discrimination analysis (Dillon and Goldstein, 1984) can be used to compact the original features into a set of uncorrelated linear discriminant functions. This procedure makes it easier to perform texture-based searches in a database by reducing the dimensionality of the discriminant space. The authors also examine the effects of varying training class size, the number of training classes, the dimension of the discriminant space and number of energy measures used for classification. The authors hope that the performance for texture discrimination of these simple energy-based features will allow images in a database to be efficiently and effectively indexed by contents of their textured regions. >

Exploratory Multivariate Analysis in Archaeology

Abstract: This text explains to new readers the various methods of multivariate analysis used in archaeological practice. It focuses on the techniques available, including: principal component analysis; correspondence analysis; cluster analysis; and discriminant analysis. Critically reviewing their use in practice, the book describes other areas in which they could be usefully applied. Methods where software packages are available are emphasized.

Recognizing text genres with simple metrics using discriminant analysis

Abstract: A simple method for categorizing texts into pre-determined text genre categories using the statistical standard technique of discriminant analysis is demonstrated with application to the Brown corpus. Discriminant analysis makes it possible use a large number of parameters that may be specific for a certain corpus or information stream, and combine them into a small number of functions, with the parameters weighted on basis of how useful they are for discriminating text genres. An application to information retrieval is discussed.

Marginal Modeling of Correlated Ordinal Data Using a Multivariate Plackett Distribution

Abstract: An extension of the bivariate model suggested by Dale is proposed for the analysis of dependent ordinal categorical data. The so-called multivariate Dale model is constructed by first generalizing the bivariate Plackett distribution to any dimensions. Because the approach is likelihood based, it satisfies properties that are not fulfilled by other popular methods, such as the generalized estimating equations approach. The proposed method models both the marginal and the association structure in a flexible way. The attractiveness of the multivariate Dale model is illustrated in three key examples, covering areas such as crossover trials, longitudinal studies with patients dropping out from the study, and discriminant analysis applications. The differences and similarities with the generalized estimating approach are highlighted.

New Approaches in Classification and Data Analysis

Abstract: Classification and Clustering: Problems for the Future.- From classifications to cognitive categorization: the example of the road lexicon.- A review of graphical methods in Japan-from histogram to dynamic display.- New Data and New Tools: A Hypermedia Environment for Navigating Statistical Knowledge in Data Science.- On the logical necessity and priority of a monothetic conception of class, and on the consequent inadequacy of polythetic accounts of category and categorization.- Research and Applications of Quantification Methods in East Asian Countries.- Algorithms for a geometrical P.C.A. with the L1-norm.- Comparison of hierarchical classifications.- On quadripolar Robinson dissimilarity matrices.- An Ordered Set Approach to Neutral Consensus Functions.- From Apresjan Hierarchies and Bandelt-Dress Weak hierarchies to Quasi-hierarchies.- Spanning trees and average linkage clustering.- Adjustments of tree metrics based on minimum spanning trees.- The complexity of the median procedure for binary trees.- A multivariate analysis of a series of variety trials with special reference to classification of varieties.- Quality control of mixture. Application: The grass.- Mixture Analysis with Noisy Data.- Locally optimal tests on spatial clustering.- Choosing the Number of Clusters, Subset Selection of Variables, and Outlier Detection in the Standard Mixture-Model Cluster Analysis.- An examination of procedures for determining the number of clusters in a data set.- The gap test: an optimal method for determining the number of natural classes in cluster analysis.- Mode detection and valley seeking by binary morphological analysis of connectivity for pattern classification.- Interactive Class Classification Using Types.- K-means clustering in a low-dimensional Euclidean space.- Complexity relaxation of dynamic programming for cluster analysis.- Partitioning Problems in Cluster Analysis: A Review of Mathematical Programming Approaches.- Clusters and factors: neural algorithms for a novel representation of huge and highly multidimensional data sets.- Graphs and structural similarities.- A generalisation of the diameter criterion for clustering.- Percolation and multimodal data structuring.- Classification and Discrimination Techniques Applied to the Early Detection of Business Failure.- Recursive Partition and Symbolic Data Analysis.- Interpretation Tools For Generalized Discriminant Analysis.- Inference about rejected cases in discriminant analysis.- Structure Learning of Bayesian Networks by Genetic Algorithms.- On the representation of observational data used for classification and identification of natural objects.- Alternative strategies and CATANOVA testing in two-stage binary segmentation.- Alignment, Comparison and Consensus of Molecular Sequences.- An Empirical Evaluation of Consensus Rules for Molecular Sequences.- A Probabilistic Approach To Identifying Consensus In Molecular Sequences.- Applications of Distance Geometry to Molecular Conformation.- Classification of aligned biological sequences.- Use of Pyramids in Symbolic Data Analysis.- Proximity Coefficients between Boolean symbolic objects.- Conceptual Clustering in Structured Domains: A Theory Guided Approach.- Automatic Aid to Symbolic Cluster Interpretation.- Symbolic Clustering Algorithms using Similarity and Dissimilarity Measures.- Feature Selection for Symbolic Data Classification.- Towards extraction method of knowledge founded by symbolic objects.- One Method of Classification based on an Analysis of the Structural Relationship between Independent Variables.- The Integration of Neural Networks with Symbolic Knowledge Processing.- Ordering of Fuzzy k-Partitions.- On the Extension of Probability Theory and Statistics to the Handling of Fuzzy Data.- Fuzzy Regression.- Clustering and Aggregation of Fuzzy Preference Data: Agreement vs. Information.- Rough Classification with Valued Closeness Relation.- Representing proximities by network models.- An Eigenvector Algorithm to Fit lp-Distance Matrices.- A non linear approach to Non Symmetrical Data Analysis.- An Algorithmic Approach to Bilinear Models for Two-Way Contingency Tables.- New Approaches Based on Rankings in Sensory Evaluation.- Estimating failure times distributions from censored systems arranged in series.- Calibration Used as a Nonresponse Adjustment.- Least Squares Smoothers and Additive Decomposition.- High Dimensional Representations and Information Retrieval.- Experiments of Textual Data Analysis at Electricite de France.- Conception of a Data Supervisor in the Prospect of Piloting Management Quality of Service and Marketing.- Discriminant Analysis Using Textual Data.- Recent Developments in Case Based Reasoning: Improvements of Similarity Measures.- Contiguity in discriminant factorial analysis for image clustering.- Exploratory and Confirmatory Discrete Multivariate Analysis in a Probabilistic Approach for Studying the Regional Distribution of Aids in Angola.- Factor Analysis of Medical Image Sequences (FAMIS): Fundamental principles and applications.- Multifractal Segmentation of Medical Images.- The Human Organism-a Place to Thrive for the Immuno-Deficiency Virus.- Comparability and usefulness of newer and classical data analysis techniques. Application in medical domain classification.- The Classification of IRAS Point Sources.- Astronomical classification of the Hipparcos input catalogue.- Group identification and individual assignation of stars from kinematical and luminosity parameters.- Specific numerical and symbolic analysis of chronological series in view to classification of long period variable stars.- Author and Subject Index.

Neural Networks, Decision Tree Induction and Discriminant Analysis: an Empirical Comparison

Abstract: This paper presents an empirical comparison of three classification methods: neural networks, decision tree induction and linear discriminant analysis. The comparison is based on seven datasets with different characteristics, four being real, and three artificially created. Analysis of variance was used to detect any significant differences between the performance of the methods. There is also some discussion of the problems involved with using neural networks and, in particular, on overfitting of the training data. A comparison between two methods to prevent overfitting is presented: finding the most appropriate network size, and the use of an independent validation set to determine when to stop training the network.

Multivariate QSAR analysis of a skin sensitization database.

Abstract: There is a regulatory requirement for the potential of a new chemical to cause skin sensitization to be assessed. This requirement is presently fulfilled by the use of animal tests. In this study a data base of heterogeneous organic compounds from the guinea pig maximization test has been subjected to multivariate QSAR analysis. The compounds were described both by whole molecule parameters and structural features associated with likely sites of reactivity. Principal component analysis was applied to the data set and although it functions reasonably well to reduce the dimensionality of a large data matrix, it is only moderately useful as a predictive tool when descriptors were chosen rationally. Stepwise discriminant analysis produces a fourteen parameter model, of which twelve were structural features associated with reactivity. This however predicts only 82.6% of compounds correctly after cross validation. There is trend for the linear discriminant analysis model to predict compounds as non sensitizers, suggesting that the parameters incorporated were not wholly suitable for discriminating between the two classes. Another criticism of linear discriminant analysis is that it may be unable to cope with the likely embedded data structure. With this in mind, the structural alerts may be better employed in an expert system, to identify potential hazard, where they will not suffer the limitations of a statistical model.

A methodological note on diversity measurement

Abstract: This research addresses the question, do categorical and continuous measures capture the same construct of diversity? Using analysis of variance, cluster analysis, and discriminant analysis, we investigated whether continuous measures (entropy and product count) differentiate between diversification categories, whether continuous measures converted to categories and subjectively assigned categories classified companies similarly, and whether continuous and categorical measures predicted similar diversity–performance relationships. We concluded that the techniques were associated but did not yield the same performance predictions. For researchers investigating diversity–performance relationships, choice of measurement technique will influence research results. Our research results suggest that attempts to combine categorical and continuous techniques as a way to overcome the limitations of both methods is not appropriate.

Discrimination between coastal subenvironments using textural characteristics

Abstract: The objective of this study was to discriminate between modern beach subenvironments based on textural characteristics obtained using the graphical (percentile) method, the moment method, and the log-hyperbolic distribution (LHD). A total of 126 surface sedimentation units were sampled at the nodes of a 21 x 6 rectangular grid (1000 m2) on a carbonate sand beach, Oahu, Hawaii. Sampling was conducted at low energy conditions from the lower foreshore to the backshore. Non-parametric discriminant analysis was used as an objective tool in defining distinct subenvironments. Confidence bands around the canonical variates derived from the graphic mean, sorting, skewness and kurtosis indicated four separate subenvironments (lower foreshore, mid-foreshore, upper foreshore and backshore). Three distinct subenvironments were identified using the mean, sorting (standard deviation) and skewness measures derived by the method of moments. A similar subenvironment distinction was obtained using five statistics of the LHD (gamma, γ; nu ν; delta, δ; tau, τ; and xi, ξ). No significant difference was noted in textural characteristics between the upper foreshore and backshore zones, and these zones were grouped into one subenvironment. These results indicate that different process scenarios would be needed to explain different subenvironment partitioning based simply on the approach adopted. Discriminant analysis indicated that fewer subenvironment samples were misclassified and separation distances between subenvironments in bivariate canonical plots were greater for the standard moment measures compared with the statistics derived from fitting the computationally intensive LHD model. Examination of the mass frequency grain size distributions indicated that the LHD was generally the most appropriate model. These observations were confirmed by the hyperbolic shape triangle which indicated that the LHD rather than the more commonly used log-normal distribution was generally optimal in describing sediments. These results support the use of the LHD statistical measures in subenvironment discrimination over the graphic-inclusive measures.

Method of generating dictionary for pattern recognition and pattern recognition method using the same

Abstract: A reference pattern vector is obtained from training patterns belonging to each class and is held as a parameter of an original distance function in a distance dictionary. A discriminant function is defined which has, as variables, the difference between respective corresponding components of a feature vector of each training pattern and the corresponding reference pattern vector and the square of the difference. Training patterns of all classes are discriminated with the original distance function and a rival pattern set, which includes patterns misclassified as belonging to a respective class, is derived from the results of discrimination of the training patterns. A discriminant analysis is made between the training pattern set of each class and the corresponding rival pattern set to thereby determine parameters of the discriminant function, which are held in a discriminant dictionary. The original distance function and the discriminant function are additively coupled together by a predetermined coupling coefficient to define a learned distance function, which is used to discriminate the training patterns to update the learned distance function.

Classification of cervical cell nuclei using morphological segmentation and textural feature extraction

Abstract: This paper presents preliminary results for the classification of Pap Smear cell nuclei, using gray level co-occurrence matrix (GLCM) textural features. We outline a method of nuclear segmentation using fast morphological gray-scale transforms. For each segmented nucleus, features derived from a modified form of the GLCM are extracted over several angle and distance measures. Linear discriminant analysis is performed on these features to reduce the dimensionality of the feature space, and a classifier with hyper-quadric decision surface is implemented to classify a small set of normal and abnormal cell nuclei. Using 2 features, we achieve a misclassification rate of 3.3% on a data set of 61 cells. >

A Comparison of Discriminant Analysis and Logistic Regression

Abstract: Logistic regression has emerged as a robust alternative to discriminant analysis. This paper compares both statistical procedures in terms of model specification, assessment of fit, and interpretation. Although both statistics performed well as classification techniques, the logistic regression model was more parsimonious and easier to interpret. The author concludes that when choosing between these two statistics, social work researchers should consider: (1) the purpose of the analysis (classification or description); (2) the characteristics of the sample; and (3) the tenability of assumptions.

A new, cellular automaton-based, nearest neighbor pattern classifier and its VLSI implementation

Abstract: A new, parallel, nearest-neighbor (NN) pattern classifier, based on a 2D Cellular Automaton (CA) architecture, is presented in this paper. The proposed classifier is both time and space efficient, when compared with already existing NN classifiers, since it does not require complex distance calculations and ordering of distances, and storage requirements are kept minimal since each cell stores information only about its nearest neighborhood. The proposed classifier produces piece-wise linear discriminant curves between clusters of points of complex shape (nonlinearly separable) using the computational geometry concept known as the Voronoi diagram, which is established through CA evolution. These curves are established during an "off-line" operation and, thus, the subsequent classification of unknown patterns is achieved very fast. The VLSI design and implementation of a nearest neighborhood processor of the proposed 2D CA architecture is also presented in this paper. >

Speech recognition system accommodating different sources

Abstract: The speech recognition system disclosed herein obtains improved recognition accuracy by employing recognition models which are discriminatively trained from a data base comprising training data from different sources, e.g., both male and female voices. A linear discriminant analysis is performed on the training data using expanded matrices in which sources are identified or labelled. The linear discriminant analysis yields respective transforms for the different sources which however map the different sources onto a common vector space in which the vocabulary models are defined.

The prediction of human exons by oligonucleotide composition and discriminant analysis of spliceable open reading frames.

Abstract: Discriminant analysis is applied to the problem of recognition 5"-, internal and 3’-exons in human DNA sequences. Specific recognition functions were developed for revealing extras of particular types.The method based on a splice site prediction algorithm that uses the linear Fisher discrirninant t~ combine the inf~Jrmation about significant triplet frequencies of various l’unctiomd parts ~f splice sile regimls and preferellces tfl ~ligunuclec~tides in protein ct~ding and il|ll’Oll rcgitms (.";

Near Infrared Spectroscopy for Classification of Iberian Pig Carcasses Using an Artificial Neural Network

Abstract: Artificial neural networks (ANNs) have demonstrated their usefulness in near infrared (NIR) reflection and transmittance spectroscopy for quantitative prediction. The new approach presented here considers the use of ANNs for qualitative classification. Four forms of neural networks (a competitive network using the learning vector quantisation, LVQ learning rule; a backpropagation network using the extended delta-bar-delta, EDBD rule; a network with direct random search, DRS; and a simple competitive linear network, CL) have been tested for classification of 118 fat samples from Iberian pig carcasses into three different price groups. An ANN using the LVQ learning rule has been found to be the best in terms of classification error size. The classification ability of the LVQ network has been evaluated against discriminant analysis, one of the most used methods for NIR spectroscopic qualitative analysis.

Classification of wheat varieties by isoelectric focusing patterns of gliadins and neural network

Abstract: Classification of wheat varieties, using isoelectric focusing patterns of the gliadins, image processing and neural networks, is described. The method was compared to a statistical classification method, discriminant analysis. The isoelectric point and the area of each band were calculated by image processing. Different methods of presenting the electrophoretic patterns to the neural network were studied. The most effective method was transformation of the electrophoretic pattern to a small (11 × 47 pixels) representation of the original digitized image, which was presented to the neural network as a vector. The neural network was trained with a number of patterns and tested with new patterns from different electrophoretic runs of the same wheat varieties. In this study we used ten different wheat varieties and the neural network was able to classify 95.5% of the patterns correctly. The statistical classification method classified the same data set 91.8% correctly. We conclude that both the neural network and discriminant analysis were able to classify the patterns correctly with a high degree of certainty. The patterns that were misclassified were indistinguishable by visual inspection.