Piotr A. Kowalski

Training Neural Networks with Krill Herd Algorithm

In recent times, several new metaheuristic algorithms based on natural phenomena have been made available to researchers. One of these is that of the Krill Herd Algorithm (KHA) procedure. It contains many interesting mechanisms. The purpose of this article is to compare the KHA optimization algorithm used for learning an artificial neural network (ANN), with other heuristic methods and with more conventional procedures. The proposed ANN training method has been verified for the classification task. For that purpose benchmark examples drawn from the UCI Machine Learning Repository were employed with Classification Error and Sum of Square Errors being used as evaluation criteria. It has been concluded that the application of KHA offers promising performance—both in terms of aforementioned metrics, as well as time needed for ANN training.

Interval probabilistic neural network

Automated classification systems have allowed for the rapid development of exploratory data analysis. Such systems increase the independence of human intervention in obtaining the analysis results, especially when inaccurate information is under consideration. The aim of this paper is to present a novel approach, a neural networking, for use in classifying interval information. As presented, neural methodology is a generalization of probabilistic neural network for interval data processing. The simple structure of this neural classification algorithm makes it applicable for research purposes. The procedure is based on the Bayes approach, ensuring minimal potential losses with regard to that which comes about through classification errors. In this article, the topological structure of the network and the learning process are described in detail. Of note, the correctness of the procedure proposed here has been verified by way of numerical tests. These tests include examples of both synthetic data, as well as benchmark instances. The results of numerical verification, carried out for different shapes of data sets, as well as a comparative analysis with other methods of similar conditioning, have validated both the concept presented here and its positive features.

Clustering using flower pollination algorithm and Calinski-Harabasz index

Task of clustering, that is data division into homogeneous groups represents one of the elementary problems of contemporary data mining. Cluster analysis can be approached through variety of methods based on statistical inference or heuristic techniques. Recently algorithms employing novel meta-heuristics are of special interest - as they can effectively tackle the problem under consideration which is known to be NP-hard. The paper studies the application of nature-inspired Flower Pollination Algorithm for clustering with internal measure of Calinski-Harabasz index being used as optimization criterion. Along with algorithms description its performance is being evaluated over a set of benchmark instances and compared with the one of well-known K-means procedure. It is concluded that the application of introduced technique brings very promising outcomes. The discussion of obtained results is followed by areas of possible improvements and plans for further research.

Sensitivity Analysis for Probabilistic Neural Network Structure Reduction

In this paper, we propose the use of local sensitivity analysis (LSA) for the structure simplification of the probabilistic neural network (PNN). Three algorithms are introduced. The first algorithm applies LSA to the PNN input layer reduction by selecting significant features of input patterns. The second algorithm utilizes LSA to remove redundant pattern neurons of the network. The third algorithm combines the proposed two and constitutes the solution of how they can work together. PNN with a product kernel estimator is used, where each multiplicand computes a one-dimensional Cauchy function. Therefore, the smoothing parameter is separately calculated for each dimension by means of the plug-in method. The classification qualities of the reduced and full structure PNN are compared. Furthermore, we evaluate the performance of PNN, for which global sensitivity analysis (GSA) and the common reduction methods are applied, both in the input layer and the pattern layer. The models are tested on the classification problems of eight repository data sets. A 10-fold cross validation procedure is used to determine the prediction ability of the networks. Based on the obtained results, it is shown that the LSA can be used as an alternative PNN reduction approach.

Clustering based on the Krill Herd Algorithm with selected validity measures

This paper describes a new approach to metaheuristic-based data clustering by means of Krill Herd Algorithm (KHA). In this work, KHA is used to find centres of the cluster groups. Moreover, the number of clusters is set up at the beginning of the procedure, and during the subsequent iterations of the optimization algorithm, particular solutions are evaluated by selected validity criteria. The proposed clustering algorithm has been numerically verified using twelve data sets taken from the UCI Machine Learning Repository. Additionally, all cases of clustering were compared with the most popular method of k-means, through the Rand Index being applied as a validity measure.

Weighted probabilistic neural network

Abstract In this work, the modification of the probabilistic neural network (PNN) is proposed. The traditional network is adjusted by introducing the weight coefficients between pattern and summation layer. The weights are derived using the sensitivity analysis (SA) procedure. The performance of the weighted PNN (WPNN) is examined in data classification problems on benchmark data sets. The obtained WPNN’s efficiency results are compared with these achieved by a modified PNN model put forward in literature, the original PNN and selected state-of-the-art classification algorithms: support vector machine, multilayer perceptron, radial basis function neural network, k-nearest neighbor method and gene expression programming algorithm. All classifiers are collated by computing the prediction accuracy obtained with the use of a k-fold cross validation procedure. It is shown that in seven out of ten classification cases, WPNN outperforms both the weighted PNN classifier introduced in literature and the original model. Furthermore, according to the ranking statistics, the proposed WPNN takes the first place among all tested algorithms.

The column-oriented data store performance considerations

The massive amounts of data processed by information systems raise the importance of detailed database performance analysis. Column-oriented data stores are becoming increasingly popular in big data appliances. This paper identifies database performance factors on the basis of empirical studies on a custom implementation. To summarize the research, a simple performance mathematical model has been created.

Modification of the probabilistic neural network with the use of sensitivity analysis procedure

In this article, the modified probabilistic neural network (MPNN) is proposed. The network is an extension of conventional PNN with the weight coefficients introduced between pattern and summation layer of the model. These weights are calculated by using the sensitivity analysis (SA) procedure. MPNN is employed to the classification tasks and its performance is assessed on the basis of prediction accuracy. The effectiveness of MPNN is also verified by analyzing its results with these obtained for both the original PNN and commonly known classification algorithms: support vector machine, multilayer perceptron, radial basis function network and k-Means clustering procedure. It is shown that the proposed modification improves the prediction ability of the PNN classifier.

Discrimination of Wheat Grain Varieties Using X-Ray Images

A study was conducted so as to develop a methodology for wheat variety discrimination and identification by way of image analysis techniques. The main purpose of this work was to determine a crucial set of parameters with respect to wheat grain morphology which best differentiate wheat varieties. To achieve better performance, the study was done by means of multivariate discriminant analysis. This utilized both forward and backward stepwise procedures based on various sets of geometric features. These parameters were extracted from the digitized X-ray images of wheat kernels obtained for three wheat varieties: Canadian, Kama, and Rosa. In our study, we revealed that selected combinations of geometric features permitted discriminant analysis to achieve a recognition rate of 89–96 %. We then compared the correctness of classification with results obtained by way of employing the nonparametric approach. The discriminant analysis proved effective in differentiating wheat varieties.

Computational modelling of cormorant swarm

Abstract The great cormorant ( Phalacrocorax carbo ) is a species with a strong impact on the environment in the areas inhabited by breeding colonies or migrating colonies. Depending on the size of colonies, destruction of tree cover, soil sterilization due to the supply of enormous amounts of aggressive faeces and overpreying on fish from nearby water bodies can be observed. In this study, an iterative algorithm for modelling the annual life cycle of a swarm of cormorants was described. The procedure was based on a mathematical model of a herd of birds, taking into account both fixed biological factors, such as food demand, availability of habitats and the reproductive cycle, and random factors occurring in the environment, e.g., flooding, storms or human activity. Additionally, the algorithm included the variable time of inhabitance depending on the duration of the ice cover over water bodies, and the possibility of conducting a killing programme. The proposed procedure was tested and verified positively using data obtained from the literature.