Extension of Decision Tree Algorithm for Stream Data Mining Using Real Data

Recently, because of increasing amount of data in the society, data stream mining targeting large scale data has attracted attention. The data mining is a technology of discovery new knowledge and patterns from the massive amounts of data, and what the data correspond to data stream is data stream mining. In this paper, we propose the feature selection with online decision tree. At first, we construct online type decision tree to regard credit card transaction data as data stream on data stream mining. At second, we select attributes thought to be important for detection of illegal use. We apply VFDT (Very Fast Decision Tree learner) algorithm to online type decision tree construction

Constraint satisfaction on dynamic environments by the means of coevolutionary genetic algorithms

We discuss adaptability of evolutionary computations in dynamic environments. We introduce two classes of dynamic environments which are utilizing the notion of constraint satisfaction problems: changeover and gradation. The changeover environment is a problem class which consists of a sequence of the constraint networks with the same nature. On the other hand, the gradation environment is a problem class which consists of a sequence of the constraint networks such that the sequence is associated with two constraint networks, i. e., initial and target, and all constraint networks in the sequence metamorphosis from the initial constraint network to the target constraint network. We compare coevolutionary genetic algorithms with SGA in computational simulations. Experimental results on the above dynamic environments confirm us the effectiveness of our approach, i.e., coevolutionary genetic algorithm</p

Vertical Distribution of Plant Species with Binucleate or Trinucleate Pollen Influenced by Ultraviolet Radiation and Temperature.

The number of pollen nuclei was determined on 133 species distributed along the slope of mountain, and the number of species with trinucleate pollen had a tendency to increase at high altitudes more than at low altitudes. From the relation between the number of pollen nuclei and the strength of the environmental stress, species with trinucleate pollen were statistically concluded to distribute under the higher stress of UV radiation or temperature than the case of species with binucleate pollen, and the distribution in trinucleate species was closely related to temperature more than UV radiation. Various physiological and biochemical characters in trinucleate pollen, which have been suggested to be effective to avoid UV radiation stress, seem to be in the same situation also to temperature stress.Article信州大学理学部紀要 24(2): 1-12(1990)departmental bulletin pape

News summary system for Web news site

We propose the system that offers only the article that is the relation to topics to the user in this research. When the user wants to read the article that is the relation to topics, the user must click the link to the article. Therefore, it is difficult for the user to read only the article related to topics. Moreover, there is the article that is similar to each other content or article. Therefore, user must read the article that is similar to other article. We propose the algorithm to find similar articles. For the proposed system, we use the feature of reported articles. There is an outline of the entire article at the beginning of reported articles

Feature Selection in Large Scale Data Stream for Credit Card Fraud Detection

There is increased interest in accurate model acquisition from large scale data streams. In this paper, because we have focused attention on time-oriented variation, we propose a method contracting time-series data for data stream. Additionally, our proposal method employs the combination of plural simple contraction method and original features. In this experiment, we treat a real data stream in credit card transactions because it is large scale and difficult to classify. This experiment yields that this proposal method improves classification performance according to training data. However, this proposal method needs more generality. Hence, we'll improve generality with employing the suitable combination of a contraction method and a feature for the feature in our proposal method

A new fitness function for discovering a lot of satisfiable solutions in constraint satisfaction problems

In this paper, we discuss how many satisfiable solutions a genetic algorithm can find in a problem instance of a constraint satisfaction problems in a single execution. Hence, we propose a framework for a new fitness function which can be applied to traditional fitness functions. However, the mechanism of the proposed fitness function is quite simple, and several experimental results on a variety of instances of general constraint satisfaction problems demonstrate the effectiveness of the proposed fitness function</p

Perception-action rule acquisition by coevolutionary fuzzy classifier system

Recently, many researchers have studied the techniques in applying a fuzzy classifier system (FCS) to control mobile robots, since the FCS can easily treat continuous inputs, such as sensors and images by using a fuzzy number. By using the FCS, however, only reflective rules are acquired. Thus, in the proposed approach, an additional genetic algorithm is incorporated in order to search for strategic knowledge, i.e., the sequence of effective activated rules in the FCS. Therefore, the proposed method consists of two modules: an ordinal FCS and the genetic algorithm. Computational experiments based on WEBOTS, one of the Khepera robot simulators, confirm the effectiveness of the proposed method</p

Coevolutionary genetic algorithm for constraint satisfaction with a genetic repair operator for effective schemata formation

We discuss a coevolutionary genetic algorithm for constraint satisfaction. Our basic idea is to explore effective genetic information in the population, i.e., schemata, and to exploit the genetic information in order to guide the population to better solutions. Our coevolutionary genetic algorithm (CGA) consists of two GA populations; the first GA, called “H-GA”, searches for the solutions in a given environment (problem), and the second GA, called “P-GA”, searches for effective genetic information involved in the H-GA, namely, good schemata. Thus, each individual in P-GA consists of alleles in H-GA or “don't care” symbol representing a schema in the H-GA. These GA populations separately evolve in each genetic space at different abstraction levels and affect with each other by two genetic operators: “superposition” and “transcription”. We then applied our CGA to constraint satisfaction problems (CSPs) incorporating a new stochastic “repair” operator for P-GA to raise the consistency of schemata with the (local) constraint conditions in CSPs. We carried out two experiments: First, we examined the performance of CGA on various “general” CSPs that are generated randomly for a wide variety of “density” and “tightness” of constraint conditions in the CSPs that are the basic measures of characterizing CSPs. Next, we examined “structured” CSPs involving latent “cluster” structures among the variables in the CSPs. For these experiments, computer simulations confirmed us the effectiveness of our CGA</p