Using Data Structure Properties in Decision Tree Classifier Design
This paper studies the techniques of performance enhancement for decision tree classifiers (DTC) that are based on data structure analysis. To improve the performance of DTC, two methods are used - class decomposition that uses the structure of class density and taxonomy based DTC design that uses interactions between attribute values. The paper shows experimental exploration of the methods, their strengths and imperfections and also outlines the directions for further research.
Arnis Kirshners, Galina Kuleshova and Arkady Borisov
Demand Forecasting Based on the Set of Short Time Series
This paper addresses the task of short historical time series and discrete descriptive parameters processing aimed at making demand forecast only on the basis of new product describing parameters. Several data mining methods are used for data processing including data extraction, pre-processing, cluster analysis and classification. Data preparation for data mining processes is made with user-defined parameters entered in the forecasting system. In the selected short historical time series the membership of an object in any class, which is a basis for creating prototypes, is determined using clustering. The k-means clustering algorithm is employed for finding the optimal number of clusters in the sample. The number of clusters is determined on the basis of the mean absolute error. As a result of classification, using inductive decision trees, a correlation between the prototype produced in the course of clustering and product describing parameters is determined. For new product demand clustering, a decision tree obtained as a result of classification is used. New product describing parameters are then projected on the tree, and a tree leave indicating the number of the prototype produced by means of clustering is found. The prototype curve structure depicts possible demand for a new product for the next period.
Pavels Osipovs, Andrejs Rinkevics, Galina Kuleshova and Arkady Borisov
This paper examines the possibility of using Markov chains when constructing a profile of author’s writing style. Thus, the constructed profile can be then used to analyze other texts and calculate their level of similarity. The extraction of the unique profile of text writing style that is characteristic of a specific human can be a topical task in many spheres of human activity. As an example, the task of detecting authorship for scientific and fiction texts can be mentioned. The paper describes a basic theoretical apparatus used for profile construction, software implementation of the experimental system as well as the experiments made and provides experimental results and their analysis.
Artificial neural networks (ANNs) are well known for their classification abilities. Although choosing hyperparameters such as neuron layer count and size can be a quite tedious task. Pruning approaches assume that a sufficiently large ANN has already been trained and can be simplified with acceptable classification accuracy loss. The current paper presents a node pruning algorithm and gives experimental results for pruned network accuracy rates versus their non-pruned counterparts.
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nieru slimîbas stadiju klasifikâcija [Classification of the stages of chronic kidney disease]. Grâm.: Nieru transplantâcija (7.-16. lpp.). Rîga: Nacionâlais apgâds.
Čeròevskis, H. (2008). Kâ iet bojâ nieres [How kidneys are perishing]. Latvijas ârsts , Nr. 7/8, 10-15.
Lewey, A. S., Inker, L.A. (2011). Definition and staging of chronic kidney disease, UpToDate. www. uptodate.com/contents /definition and staging of chronic kidney disease/view=print
Kuzminskis, V., Rosenberg, M., Cernevskis, H., Bumblyte, I. A. (2011
From Inductive Learning Towards Interactive Inductive Learning
Growing amount of information in the world encourage the use of automatic data processing techniques that reduce humans routine work. There is a wide range of methods used for machine learning; however inductive learning algorithms are preferable in the systems where understanding of decision making steps and further processing of results is needed, for instance the expert systems, where the rules induced by learning algorithms can be used. As the classification tasks are getting more complicated computer program may not make enough informed decision by itself. In such situations collaborative approach between machine and systems user (expert) would be useful. Inductive learning system learns classification from training examples and uses induced rules for classifying new cases. If a decision cannot be inferred from rules base, a guess is performed. Interactive inductive system in uncertain conditions could ask human for decision and improve its knowledge base with the rule derived from this human-made decision. The paper summarises approaches discussed in related works and classifies them by the phase in inductive learning process in which the human interaction appears. As a result a new approach to interactive inductive system is presented. Conceptual example of topographical map classification using this system is demonstrated.
The usual aim of the modern computer-aided system modelling is to improve a connection between software model and code components. Therefore, the task of a diagram import/export becomes very important during software development. Layouting of diagrams after importation from another tool and application plays the main role. Authors of this paper describe some concepts, which are currently being considered in the area of diagram layouting and indicate several problems and their potential solutions for use in the development of CASE tools.
This article focuses on cluster stability evaluation to assess the characteristics of the dataset and the subclasses found in class decomposition. The evaluation is an iterative process, making small changes to the dataset in every step and reapplying the cluster analysis. These small changes (removing one object from the dataset is repeated for 20 iterations in this case) should not have any impact on clusters if they are stable (meaning that other objects that were not removed stay in the same clusters as in the full clustering).
Edgars Vasiļevskis, Aleksejs Miščuks and Indulis Vanags
Special Devices for Regional Anaesthesia
The rapid growth of regional anaesthesia in the last 20 years has resulted in an increasing number of technical devices. Technical resources play a very considerable role in modern regional anaesthesia and they are being introduced to facilitate the quality performance of this type of anaesthesia. The task of this survey is to summarise the most important technical devices currently utilised for regional anaesthesia and to provide a review of the history of their introduction. Technical devices for performing regional anaesthesia could be divided in seven groups: 1) neurostimulation and simple needles and catheters; 2) neurostimulator with transcutaneous nerve stimulation (TENS); 3) ultrasonograph with a device to fix the probe; 4) devices for injection of local anaesthetics, such as perfusors, patient controlled analgesia systems and elastomeric infusion systems; 5) arm, leg and instruments supports; 6) regional anaesthesia injection monitor and data register devices, and 7) catheter fastenings and auxiliary materials. It is recommended that all of the apparatus applied should be classified to make it easier to acquire more immediate and understanding, and be easy to add this knowledge to the overall checklist before starting anaesthesia. The history of the introduction of technical equipment shows how much time was necessary for the development of modern regional anaesthesia methods.