Supervised Pattern Recognition Techniques Research Articles

Genetic algorithms for the optimization of piecewise linear discriminants

The application of genetic algorithms (GAs) to the optimization of piecewise linear discriminants is described. Piecewise linear discriminant analysis (PLDA) is a supervised pattern recognition technique employed in this work for the automated classification of Fourier transform infrared (FTIR) remote sensing data. PLDA employs multiple linear discriminants to approximate a nonlinear separating surface between data categories defined in a vector space. The key to the successful implementation of PLDA is the positioning of the individual discriminants that comprise the piecewise linear discriminant. For the remote sensing application, the discriminant optimization is challenging due to the large number of input variables required and the corresponding tendency for local optima to occur on the response surface of the optimization. In this work, three implementations of GAs are configured and evaluated: a binary-coded GA (GAB), a real-coded GA (GAR), and a Simplex-GA hybrid (SGA). GA configurations are developed by use of experimental design studies, and piecewise linear discriminants for acetone, methanol, and sulfur hexafluoride are optimized (trained). The training and prediction classification results indicate that GAs area viable approach for discriminant optimization. On average, the best piecewise linear discriminant optimized by a GA is observed to classify 11% more analyte-active patterns correctly in prediction than an unoptimized piecewise linear discriminant. Discriminant optimization problems not used in the experimental design study are employed to test the stability of the GA configurations. For these cases, the best piecewise linear discriminant optimized by SGA is shown to classify 19% more analyte-active patterns correctly in prediction than an unoptimized discriminant. These results also demonstrate that the two real number coded GAs (GAR and SGA) perform better than the GAB. Real number coded GAs are also observed to execute faster and are simpler to implement.

Pattern recognition applied to gas chromatographic profiles of volatile components in three tea categories

Volatile components in unfermented green tea, semi-fermented Oolong tea and fully fermented black tea were analyzed by gas chromatography (GC) and gas chromatography-mass spectrometry (GC-MS). For differentiating, three tea categories based on their volatile components, unsupervised and supervised pattern recognition techniques were applied to the resulting GC data. Three distinct clusters each corresponding to green tea, Oolong tea and black tea were observed in the dendrogram and the principal component (PC) score plot. However, a subcluster of Oolong tea was observed in the vicinity of black tea cluster in both the dendrogram and the PC plot. The first and second PC corresponded to the fermentation products and aroma components originally contained in tea leaves, respectively. Both the partial least squares (PLS) analysis and linear discriminant analysis correctly differentiated tea samples into the three categories. (E)-2-Hexenal, the major fermentation product from unsaturated fatty acids, was the most efficient for the discrimination. Although three teas are produced from the same plant species, pattern recognition clarified the existence of the apparent quality difference among their volatile component profiles.

MRI segmentation using fuzzy clustering techniques

The authors' main contribution is to build upon their earlier efforts by expanding the tissue model concept to cover a brain volume. Furthermore, processing time is reduced and accuracy is enhanced by the use of knowledge propagation, where information derived from one slice is made available to succeeding slices as additional knowledge. The system is organized as follows. Each MR slice is initially segmented by an unsupervised fuzzy c-means clustering algorithm. Next, an expert system uses model-based recognition techniques to locate a landmark, called a focus-of attention tissue. Qualitative models of slices of brain tissue are defined and matched with their instances from imaged slices. If a significant deformation is detected in a tissue, the slice is classified to be abnormal and volume processing halts. Otherwise, the expert system locates the next focus-of-attention tissue, based on a hierarchy of expected tissues. This process is repeated until either a slice is classified as abnormal or all tissues of the slice are labeled. If the slice is determined to be abnormal, the entire volume is also considered abnormal and processing halts. Otherwise, the system will proceed to the next slice and repeat the classification steps until all slices that comprise the volume are processed. A rule-based expert system tool, CLIPS, is used to organize the system. Low level modules for image processing and high level modules for image analysis, all written in the C language, are called as actions from the right hand sides of the rules. The system described here is an attempt to provide completely automatic segmentation and labeling of normal volunteer brains. The absolute accuracy of the segmentations has not yet been rigorously established. The relative accuracy appears acceptable. Efforts have been made to segment an entire volume (rather than merging a set of segmented slices) using supervised pattern recognition techniques or unsupervised fuzzy clustering. However, there is sometimes enough data nonuniformity between slices to prevent satisfactory segmentation.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Evaluation of the required sample size in some supervised pattern recognition techniques

One of the aspects of supervised pattern recognition applications which is often ignored concerns the minimum number of samples necessary to define sufficiently reliable classification rules. For discriminating techniques, criteria are available that can be used to evaluate this minimum sample size. For class-modelling techniques, however, no attention has previously been paid to this aspect. Here, the guidelines that can be applied in the use of a discriminating supervised technique are discussed, and criteria are proposed that can be applied when class-modelling supervised techniques, particularly UNEQ, are applied.

Supervised Pattern Recognition Techniques in Quantitative Diagnostic Ultrasound

The methods of statistical pattern recognition are well suited to the problems of in vivo ultrasonic tissue characterization. This paper describes supervised pattern recognition methods for selecting features for tissue classification, calculating decision boundaries within the selected feature space, and evaluating the performance. We address the considerations of dimensionality and feature size which are important in classification problems where the underlying probability distributions are not completely known. Examples are given for the detection of diffuse liver disease in the clinical environment.

Comparison of the performance of the class modelling techniques UNEQ, SIMCA, and PRIMA

Derde, M.P. and Massart, D.L., 1988. Comparison of the performance of the class modelling techniques UNEQ, SIMCA, and PRIMA. Chemometrics and Intelligent Laboratory Systems, 4: 65-93 By means of a Monte Carlo study a systematic comparison of the supervised pattern recognition techniques of the class modelling type, UNEQ, SIMCA and PRIMA is made. In particular, the success rate of the classification decisions and the influence of the sample size on it were investigated. It was concluded that better class models are obtained when a technique is used that takes the shape of the population distribution into account. If the actual distribution cannot be determined, then use should be made of techniques that make no or only weak assumptions about the shape of the distribution. However, even then it remains worthwhile to investigate whether the variables are correlated and to take this information into account. When using SIMCA and PRIMA, attention should also be paid to the way the class models are defined: an approach that makes use of certain sample parameters such as the range of the variables or the maximum distance between a training object and the class model might lead to overly broad models, especially for large training sets.

UNEQ: A class modelling supervised pattern recognition technique

The purpose of this study is to compare the performance of UNEQ, a supervised pattern recognition technique of the classmodelling type, with other classification techniques and to demonstrate its use for a practical application, namely the classification of coals.

Supervised pattern recognition: the ideal method?

The different criteria that should be considered in selecting a supervised pattern recognition technique for a particular application are discussed. An overview is given of the most important and most frequently-used supervised techniques and the extent to which they meet the criteria. The possibilities of two rule-building expert systems are also discussed.

A Multivariate Pattern Recognition Study of Risk-Factors Indicating Postoperative Thromboembolism Despite Low-Dose Heparin in Major Abdominal Surgery

The object of the present investigation was to identify those who, among high-risk patients, would "break through" low-dose heparin prophylaxis and develop thromboembolism after major abdominal surgery. Twenty-nine variables (clinical characteristics, pre- and postoperative coagulation and fibrinolytic factors) from 19 patients with and 26 patients without thromboembolism were analyzed by means of a multivariate supervised pattern recognition technique (SIMCA). We found no statistically significant difference between patients with and without thromboembolism. Thus, in the studied group of high-risk patients it was not possible to identify a predictive index for selection of individual patients liable to develop postoperative thromboembolism despite low-dose heparin prophylaxis in major abdominal surgery.

Ulcer Recurrence after Parietal Cell Vagotomy for Duodenal Ulcer

The object of the study was to identify individual high-risk patients with regard to ulcer recurrence after parietal cell vagotomy for duodenal ulcer. The study comprises a multivariate analysis of 14 variables (age, sex, duration of symptoms, site of ulcer, pre- and post-operative acid secretion) for 37 patients with and 111 patients without recurrence 5-11 years after parietal cell vagotomy for duodenal ulcer. The data were analyzed using a supervised pattern recognition technique, SIMCA (Soft Independent Modeling of Class Analogy), which analyzes complex data as geometrical elements in a multidimensional space. We found no statistical difference between the patients with and without ulcer recurrence. Thus, no predictive value for the selection of patients liable to develop recurrent ulcer after parietal cell vagotomy was contained in the variables generally registered in current surgery.

Potential methods in pattern recognition: Part 5. ALLOC, Action-orientated Decision Making

The possibilities of action-orientated pattern recognition with the supervised pattern recognition technique, ALLOC, are discussed. The emphasis is on the importance of the definition of overlapping regions between classes as a way for obtaining more information about the separation between classes. Action-orientated classification and feature selection with ALLOC are discussed using the results obtained for two data bases concerning the characterization of the functional state of the thyroid and the determination of the origin of milk samples.