Multiple Resolution Nonparametric Classifiers

Bayesian discriminant functions provide optimal classification decision boundaries in the sense of minimizing the average error rate. An operational assumption is that the probability density functions for the individual classes are either known a priori or can be estimated from the data through the use of estimating techniques. The use of Parzen- windows is a popular and theoretically sound choice for such estimation. However, while the minimal average error rate can be achieved when combining Bayes Rule with Parzen-window density estimation, the latter is computationally costly to the point where it may lead to unacceptable run-time performance. We present the Multiple Resolution Nonparametric (MRN) classifier as a new approach for significantly reducing the computational cost of using Parzen-window density estimates without sacrificing the virtues of Bayesian discriminant functions. Performance is evaluated against a standard Parzen-window classifier on several common datasets

Классификатор биомедицинских сигналов

В статье рассматриваются особенности построения и принцип функционирования цифрового фильтра в составе нейросетевого классификатора биомедицинских сигналов. Предлагается новый подход к реализации процесса классификации с использованием дискриминантных функций.У статті розглядаються особливості побудови і принцип функціонування цифрового фільтра у складі нейромережного класифікатора біомедичних сигналів. Пропонується новий підхід до реалізації процесу класифікації з використанням дискримінантних функцій.The features of construction and operation of the digital filter in the neural-network classifier of biomedical signals are considered in the article. A new approach to the implementation of the classification process using the discriminant functions is proposed

Risk Classification Model for Design and Build Projects

The purpose of this paper is to investigate if the various risk sources in Design and Build projects can be classified into three risk groups of cost, time and quality using the discriminant analysis technique. Literature search was undertaken to review issues of risk sources, classification of the identified risks into a risk structure, management of risks and effects of risks all on Design and Build projects as well as concepts of discriminant analysis as a statistical technique. This literature review was undertaken through the use of internet, published papers, journal articles and other published reports on risks in Design and Build projects. A research questionnaire was further designed to collect research information. This research study is a survey research that utilized cross-sectional design to capture the primary data. The data for the survey was collected in Nigeria. In all forty (40) questionnaires were sent to various respondents that included Architects, Engineers, Quantity Surveyors and Builders who had used Design and Build procurement method for their recently completed projects. Responses from these retrieved questionnaires that measured the impact of risks on Design and Build were analyzed using the discriminant analysis technique through the use of SPSS software package to build two discriminant models for classifying risks into cost, time and quality risk groups. Results of the study indicate that time overrun and poor quality are the two factors that discriminate between cost, time and quality related risk groups. These two discriminant functions explain the variation between the risk groups. All the discriminating variables of cost overrun, time overrun and poor quality demonstrate some relationships with the two discriminant functions. The two discriminant models built can classify risks in Design and Build projects into risk groups of cost, time and quality. These classifications models have 72% success rate of classification of risks in Design and Build projects. These models are strongly recommended for use of clients, Design and Build contractors and Risk Managers for the management, control and mitigation of future risks in new Design and Build projects. These models will offer appreciable improvements in risk management and mitigations which can enhance better management of future Design and Build projects. This study also recommends that clients and contractors using Design and Build approach should watch out for emerging issues of cost overrun and poor quality in their projects as these can dictate classification of newly encountered risks.Keywords: Risk classification, model, Design and Build project

A system identification based approach for pulsed eddy current non-destructive evaluation

This paper is concerned with the development of a new system identification based approach for pulsed eddy current non-destructive evaluation and the use of the new approach in experimental studies to verify its effectiveness and demonstrate its potential in engineering applications

Sparse multinomial kernel discriminant analysis (sMKDA)

Dimensionality reduction via canonical variate analysis (CVA) is important for pattern recognition and has been extended variously to permit more flexibility, e.g. by "kernelizing" the formulation. This can lead to over-fitting, usually ameliorated by regularization. Here, a method for sparse, multinomial kernel discriminant analysis (sMKDA) is proposed, using a sparse basis to control complexity. It is based on the connection between CVA and least-squares, and uses forward selection via orthogonal least-squares to approximate a basis, generalizing a similar approach for binomial problems. Classification can be performed directly via minimum Mahalanobis distance in the canonical variates. sMKDA achieves state-of-the-art performance in terms of accuracy and sparseness on 11 benchmark datasets

Partial least squares discriminant analysis: A dimensionality reduction method to classify hyperspectral data

The recent development of more sophisticated spectroscopic methods allows acquisition of high dimensional datasets from which valuable information may be extracted using multivariate statistical analyses, such as dimensionality reduction and automatic classification (supervised and unsupervised). In this work, a supervised classification through a partial least squares discriminant analysis (PLS-DA) is performed on the hy- perspectral data. The obtained results are compared with those obtained by the most commonly used classification approaches