Previous studies have implemented Backpropagation Feedforward Neural Networks (BPNNs) as an alternative technique to Linear Discriminant Analysis (LDA) to predict bond ratings using relatively small data sets due to the limited number of bond ratings data available. Although these studies report the superiority of BPNNs over the LDA to predict bond ratings, they seem to ignore that BPNNs suffer...

In this paper, we propose to use probabilistic neural networks (PNNs) for classification of bacterial growth/no-growth data and modeling the probability of growth. The PNN approach combines both Bayes theorem of conditional probability and Parzen's method for estimating the probability density functions of the random variables. Unlike other neural network training paradigms, PNNs are characteri...

Embedding the original high dimensional data in a low dimensional space helps to overcome the curse of dimensionality and removes noise. The aim of this work is to evaluate the performance of three different linear dimensionality reduction techniques (DR) techniques namely principal component analysis (PCA), multi dimensional scaling (MDS) and linear discriminant analysis (LDA) on classificatio...

This paper proposes a new hybrid method named SCFE-PNN, which integrates effective subtractive clustering based features enhancement and probabilistic neural network (PNN) classifier, had been introduced for isolated Malay word recognition. The proposed method of subtractive clustering features weighting is used as a data preprocessing tool, which designs at diminishing the divergence in featur...

An approach for Fusarium diseased corn kernels recognition based on wavelet continuous transformation and probabilistic neural network (PNN) classification is presented in the paper. The near infrared diffuse reflectance characteristics are used as base for features extraction. The spectral data are fitted using continuous wavelet mexican hat transformations and their parameters are used for cl...

In this paper, the study of instability in Minimal Resource Allocation Network (MRAN) when presented with pattern classification is investigated and eliminated to a great extent. To tackle the instability problem in MRAN, an improved algorithm for pattern classification and approximation referred to as extended-MRAN (eMRAN) is introduced. In eMRAN, the ideas of localized adding criteria, locali...

A well-known and widely used model for classification and prediction is the Probabilistic Neural Network (PNN). PNN’s performance is influenced by the kernels’ spread parameters so recently several approaches have been proposed to tackle this problem. The proposed approach is a combination of two well known methods applied to PNNs. First, it incorporates a Bayesian model for the estimation of P...

Investment theory has as a major tenet the concept of efficient markets. In an efficient market all information is fully reflected in the price of a stock. As such, there is no trading strategy based on known data that can earn an abnormal profit. Using price and volume data, this design and subsequent model produces a weak form test of the efficient markets hypothesis. First, an Object-Oriente...

We propose a method for indoor versus outdoor scene classification using a probabilistic neural network (PNN). The scene is initially segmented (unsupervised) using fuzzy C-means clustering (FCM) and features based on color, texture, and shape are extracted from each of the image segments. The image is thus represented by a feature set, with a separate feature vector for each image segment. As ...

This study proposes an Artificial Neural Network (ANN) and Genetic Algorithm model for diagnostic risk factors selection in medicine. A medical disease prediction may be viewed as a pattern classification problem based on a set of clinical and laboratory parameters. Probabilistic Neural Networks (PNNs) were used to face a medical disease prediction. Genetic Algorithm (GA) was used for pruning t...