Halina Kwasnicka

Correlation-based Feature Selection Strategy in Neural Classification

One of the problems that have to be overcome in classification tasks is high data dimensionality. Therefore, dimensionality reduction techniques such as feature selection have to be employed. Feature selection involves univariate or multivariate evaluation of features with respect to the classification accuracy. Pairwise feature selection was recently proposed as a trade-off between selection process complexity and the need to analyze relationships between features. In our previous work we have proposed a correlation-based modification of the pairwise feature selection. In this paper we present the results of the experiments in which we have compared the correlation-based feature selection strategy with the unmodified pairwise approach. The experiments were performed using neural network classifiers on commonly used benchmark data sets

Long-term diffusion factors of technological development: An evolutionary model and case study

In the first part of this article, a short description of the most popular models of two competing technologies (the Fisher-Pry model and its modifications proposed by Blackman, Floyd, Sharif and Kabir) and the multi technological substitution models of Peterka and Marchetti-Nakiaeenoviae are presented. In the second section, we describe an evolutionary model of diffusion processes based on biological analogy, together with the method of its parameters’ identification using real data on technologies development. In the final sections the applications of that model to describe the real diffusion processes (namely, primary energy sources in the world energy consumption and the raw steel production in the United States) are presented. The feasibility of using the model to predict future shares of given technologies and to build alternative scenarios of future evolution of structure of the market is suggested.

Multi Population Pattern Searching Algorithm: A New Evolutionary Method Based on the Idea of Messy Genetic Algorithm

One of the main evolutionary algorithms bottlenecks is the significant effectiveness dropdown caused by increasing number of genes necessary for coding the problem solution. In this paper, we present a multi population pattern searching algorithm (MuPPetS), which is supposed to be an answer to situations where long coded individuals are a must. MuPPetS uses some of the messy GA ideas like coding and operators. The presented algorithm uses the binary coding, however the objective is to use MuPPetS against real-life problems, whatever coding schema. The main novelty in the proposed algorithm is a gene pattern idea based on retrieving, and using knowledge of gene groups which contains genes highly dependent on each other. Thanks to gene patterns the effectiveness of data exchange between population individuals improves, and the algorithm gains new, interesting, and beneficial features like a kind of “selective attention” effect.

Capillary Abnormalities Detection Using Vessel Thickness and Curvature Analysis

The growing importance of nail-fold capillaroscopy imaging as a diagnostic tool in medicine increases the need to automate this process. One of the most important markers in capillaroscopy is capillary thickness. On this basis capillaries may be divided into three separate categories: healthy , capillaries with increased loops and megacapillaries . In the paper we describe the problem of capillary thickness analysis automation. First, data is extracted from a segmented capillary image. Then feature vectors are constructed. They are given as an input for capillary classification method. We applied different classifiers in the experiments. The best achieved accuracy reaches 97%, which can be considered as very high and satisfying.

Coalition Formation in multi-agent systems—an evolutionary approach

The paper introduces solution of coalition formation problem (CFP) in multi-agents systems (MAS) based on evolutionary algorithm. The main aim of our study is to develop an evolutionary based algorithm for creation of coalitions of agent for solving assumed tasks. We describe the coding schema and genetic operators such as mutation, crossover and selection that occurred to be efficient in solution of CFP. Last part of the document provides a brief comment on our research results.

Resulted word counts optimization-A new approach for better automatic image annotation

One of major problems in image auto-annotation is the difference between the expected word counts vector and the resulted word counts vector. This paper presents a new approach to automatic image annotation-an algorithm called resulted word counts optimizer which is an extension to existing methods. An ideal annotator is defined in terms of recall quality measure. On the basis of the ideal annotator an optimization criterion is defined. It allows to reduce the difference between resulted and expected word counts vectors. The proposed algorithm can be used with various image auto-annotation algorithms because its generic nature. Additionally, it does not increase the computational complexity of the original annotation method processing phase. It changes output word probabilities according to a pre-calculated vector of correction coefficients.

Multiple Class Machine Learning Approach for an Image Auto-Annotation Problem

Image auto-annotation problem becomes more and more popular research topic. Possible applications of auto-annotation methods range from Internet search engines to medical analysis software. The important aspect is that efficient image auto-annotation systems can eliminate the need of annotating huge image collections manually, which is the only solution today. Most of methods available in the literature do not use supervised machine learning as the key component. Recent researches show that supervised machine learning can successfully compete with existing approaches. This paper presents a novel image auto-annotation algorithm based of supervised machine learning with the use of C4.5 classifiers

Correlation based feature selection method

Feature selection is an important data preprocessing step which is performed before a learning algorithm is applied. The issue that has to be taken into consideration when proposing a feature selection method is its computational complexity. Often, if the feature selection process is fast, it cannot thoroughly search the feature subset space and classification accuracy is degraded. Lately, a pairwise feature selection method was proposed as an effective trade-off between computation speed and classification accuracy. In this paper, a new feature selection method is proposed which further improves feature selection speed while preserving classification accuracy. The new method selects features individually or in a pairwise manner based on the correlations between features. Experiments conducted on several benchmark data sets prove with high statistical significance that the correlation-based feature selection method shortens computations compared to the pairwise feature selection method and produces classification errors that are not worse than those produced by existing methods.

Capillary Blood Vessel Tortuosity Measurement Using Graph Analysis

Capillaroscopy is a branch of medicine which allows to diagnose various kinds of rheumatic diseases on the basis of observation of visual properties of nail-fold capillaries. Capillaries are tiny blood vessels of various shapes and sizes. Blood vessel tortuosity is one of medical signs. The paper presents a novel blood vessel tortuosity measure designed for capillary analysis. It represents the vessel as a graph and utilizes non-directional and directional traversal algorithms.

Learning Assistant - Personalizing Learning Paths in e-Learning Environments

The paper presents an agent called Learning Assistant, which is responsible for defining individual learning paths for pupils in e-learning environment. The Assistant is able to infer using metadata described pupils and didactic materials; this inference is a basis for building the individual learning path for each pupil. To build a learning path for a new pupil the agent uses information collected during introductory tests. A SOM neural network is used for grouping similar pupils. WebTeacher is e-learning environment in which Learning Assistant works. This environment is shortly presented in the paper. Next, we present the idea of personalization-we consider the individuals pupil characteristic and a group of similar pupils characteristic. Data structures described didactic materials and pupils are also shortly explained. The performed experiments allow formulate some conclusions, they are described very shortly. Summary ends the paper.