Mapping high-dimensional data onto a relative distance plane--an exact method for visualizing and characterizing high-dimensional patterns.

We introduce a distance (similarity)-based mapping for the visualization of high-dimensional patterns and their relative relationships. The mapping preserves exactly the original distances between points with respect to any two reference patterns in a special two-dimensional coordinate system, the relative distance plane (RDP). As only a single calculation of a distance matrix is required, this method is computationally efficient, an essential requirement for any exploratory data analysis. The data visualization afforded by this representation permits a rapid assessment of class pattern distributions. In particular, we can determine with a simple statistical test whether both training and validation sets of a 2-class, high-dimensional dataset derive from the same class distributions. We can explore any dataset in detail by identifying the subset of reference pairs whose members belong to different classes, cycling through this subset, and for each pair, mapping the remaining patterns. These multiple viewpoints facilitate the identification and confirmation of outliers. We demonstrate the effectiveness of this method on several complex biomedical datasets. Because of its efficiency, effectiveness, and versatility, one may use the RDP representation as an initial, data mining exploration that precedes classification by some classifier. Once final enhancements to the RDP mapping software are completed, we plan to make it freely available to researchers.

Bleeding predisposition assessments in tonsillectomy/adenoidectomy patients using fuzzy interquartile encoded neural networks.

A fuzzy set theoretic methodology is described that serves as a classification preprocessing strategy for supervised feed-forward neural networks. This methodology, fuzzy interquartile encoding, determines the respective degrees to which a feature belongs to a collection of fuzzy sets that overlap at the respective quartile boundaries of the feature. These membership values are subsequently used in place of the original feature. This transformation has a normalizing effect on the feature space and is more robust to feature outliers. Its effectiveness is scrutinized using several synthetic data sets with various underlying distributions. Fuzzy interquartile encoding is shown to consistently improve the discriminatory power of the underlying classifiers. The methodology is also applied to two biomedical data sets relating to tonsillectomy and/or adenoidectomy patients who may or may not have had a predisposition to excessive bleeding during their operation. The features of the first data set are blood sample test results acquired from a coagulation laboratory and the class labels are one of three hemostatic defects as identified by the reference tests. The second data set consists of patient responses to queries from a bleeding tendency questionnaire. Normal and abnormal class labels were derived from a hematology expert system designed in consultation with a pediatric hematologist. Fuzzy interquartile encoding effected an 11% improvement in the classification accuracy of the underlying neural network classifier with the former data set and 18% with the latter.

EvIdent: a functional magnetic resonance image analysis system.

EvIdent (EVent IDENTification) is a user-friendly, algorithm-rich, exploratory data analysis software for quickly detecting, investigating, and visualizing novel events in a set of images as they evolve in time and/or frequency. For instance, in a series of functional magnetic resonance neuroimages, novelty may manifest itself as neural activations in a time course. The core of the system is an enhanced variant of the fuzzy c-means clustering algorithm. Fuzzy clustering obviates the need for models of the underlying requisite biological function, models that are often statistically suspect.

Fuzzy pre-processing of gold standards as applied to biomedical spectra classification.

Fuzzy gold standard adjustment is a novel fuzzy set theoretic pre-processing strategy that compensates for the possible imprecision of a well-established gold standard (reference test) by adjusting, if necessary, the class labels in the design set while maintaining the gold standard's discriminatory power. The adjusted gold standard incorporates robust within-class centroid information. This strategy was applied to biomedical data acquired from a MR spectrometer for the purpose of classifying human brain neoplasms. It is shown that consistent improvement (10-13%) to the discriminatory power of the underlying classifier is obtained when using this pre-processing strategy.

Bleeding/bruising symptomatology in children with and without bleeding disorders.

A questionnaire, designed to assess bleeding/bruising tendencies, was administered to 251 otherwise healthy children undergoing a tonsillectomy and/or adenoidectomy. 23 children with excessive bleeding during or after the operation, with a long bleeding time or who reported taking aspirin recently were excluded, to give a population of 228 non-bleeders. For comparative purposes, 31 patients with bleeding disorders (von Willebrand's disease and/or platelet function defects) were studied. A considerable proportion of "non-bleeding" children reported easy bruising (24%), had bruises at least once a week (36%) and suffered from nosebleeds (39%). The respective frequencies (67%, 68% and 69%) for children with bleeding disorders were significantly higher. Occurrence of bruises usually on more than one part of the body, frequent large bruises or hematomas were rare in "non-bleeders" (4.9%, 3.5% and 2.7% respectively), but more common in "bleeders" (38.5%, 29.6% and 21.7% respectively).