Decision Trees for Applicability of Evolution Rules in Transition P Systems

Transition P Systems are a parallel and distributed computational model based on the notion of the cellular membrane structure. Each membrane determines a region that encloses a multiset of objects and evolution rules. Transition P Systems evolve through transitions between two consecutive configurations that are determined by the membrane structure and multisets present inside membranes. Moreover, transitions between two consecutive configurations are provided by an exhaustive non-deterministic and parallel application of active evolution rules subset inside each membrane of the P system. But, to establish the active evolution rules subset, it is required the previous calculation of useful and applicable rules. Hence, computation of applicable evolution rules subset is critical for the whole evolution process efficiency, because it is performed in parallel inside each membrane in every evolution step. The work presented here shows advantages of incorporating decision trees in the evolution rules applicability algorithm. In order to it, necessary formalizations will be presented to consider this as a classification problem, the method to obtain the necessary decision tree automatically generated and the new algorithm for applicability based on it.

Using Sensitivity as a Method for Ranking the Test Cases Classified by Binary Decision Trees

Usually, data mining projects that are based on decision trees for classifying test cases will use the probabilities provided by these decision trees for ranking classified test cases. We have a need for a better method for ranking test cases that have already been classified by a binary decision tree because these probabilities are not always accurate and reliable enough. A reason for this is that the probability estimates computed by existing decision tree algorithms are always the same for all the different cases in a particular leaf of the decision tree. This is only one reason why the probability estimates given by decision tree algorithms can not be used as an accurate means of deciding if a test case has been correctly classified. Isabelle Alvarez has proposed a new method that could be used to rank the test cases that were classified by a binary decision tree [Alvarez, 2004]. In this paper we will give the results of a comparison of different ranking methods that are based on the probability estimate, the sensitivity of a particular case or both.

Evaluating Misclassification Probability Using Empirical Risk

* The work is supported by RFBR, grant 04-01-00858-a

An Information Model for Pension Fund Management

In this paper the issues of Ukrainian new three-level pension system are discussed. First, the paper presents the mathematical model that allows calculating the optimal size of contributions to the non-state pension fund. Next, the non-state pension fund chooses an Asset Management Company. To do so it is proposed to use an approach based on Kohonen networks to classify asset management companies that work in Ukrainian market. Further, when the asset management company is chosen, it receives the pension contributions of the participants of the non-pension fund. Asset Management Company has to invest these contributions profitably. This paper proposes an approach for choosing the most profitable investment project using decision trees. The new pension system has been lawfully ratified only four years ago and is still developing, that is why this paper is very important.

Classification of Paintings by Artist, Movement, and Indoor Setting Using MPEG-7 Descriptor Features

ACM Computing Classification System (1998): I.4.9, I.4.10.