Instance reduction approach to machine learning and multi-database mining

The paper proposes a heuristic instance reduction algorithm as an approach to machine learning and knowledge discovery in centralized and distributed databases. The proposed algorithm is based on an original method for a selection of reference instances and creates a reduced training dataset. The reduced training set consisting of selected instances can be used as an input for the machine learning algorithms used for data mining tasks. The algorithm calculates for each instance in the data set the value of its similarity coefficient. Values of the coefficient are used to group instances into clusters. The number of clusters depends on the value of the so called representation level set by the user. Out of each cluster only a limited number of instances is selected to form a reduced training set. The proposed algorithm uses population learning algorithm for selection of instances. The paper includes a description of the proposed approach and results of the validating experiment

An Approach to Data Reduction for Learning from Big Datasets: Integrating Stacking, Rotation, and Agent Population Learning Techniques

In the paper, several data reduction techniques for machine learning from big datasets are discussed and evaluated. The discussed approach focuses on combining several techniques including stacking, rotation, and data reduction aimed at improving the performance of the machine classification. Stacking is seen as the technique allowing to take advantage of the multiple classification models. The rotation-based techniques are used to increase the heterogeneity of the stacking ensembles. Data reduction makes it possible to classify instances belonging to big datasets. We propose to use an agent-based population learning algorithm for data reduction in the feature and instance dimensions. For diversification of the classifier ensembles within the rotation also, alternatively, principal component analysis and independent component analysis are used. The research question addressed in the paper is formulated as follows: does the performance of a classifier using the reduced dataset be improved by integrating the data reduction mechanism with the rotation-based technique and the stacking

Evaluation of the on-line commercial service quality based on association rules

The existing approaches to the evaluation of on-line commercial services quality include various quality indicators. The application of multiple attributes for quality evaluation enables and involves specialized analysis techniques to carry it out. This article proposes to utilize association rules in the quality evaluation of the on-line services. The analysis carried out of the discovered association rules has provided interesting dependencies and relationships on the individual characteristics of the on-line commercial service. On the basis of such on analysis, conclusions can be made regarding the general quality of the on-line commercial services. The discovered dependencies and connections can be used in shaping online commercial service quality. Conclusions from the analysis of the association rules can therefore be used to improve the on-line commercial service quality comprehensively which can lead to the higher satisfaction of e-customer

EVALUATION OF THE ON-LINE COMMERCIAL SERVICE QUALITY BASED ON ASSOCIATION RULES

The existing approaches to the evaluation of on-line commercial services quality include various quality indicators. The application of multiple attributes for quality evaluation enables and involves specialized analysis techniques to carry it out. This article proposes to utilize association rules in the quality evaluation of the on-line services. The analysis carried out of the discovered association rules has provided interesting dependencies and relationships on the individual characteristics of the on-line commercial service. On the basis of such on analysis, conclusions can be made regarding the general quality of the on-line commercial services. The discovered dependencies and connections can be used in shaping online commercial service quality. Conclusions from the analysis of the association rules can therefore be used to improve the on-line commercial service quality comprehensively which can lead to the higher satisfaction of e-customer