Kazunori Iwata

Text Classification by Combining Different Distance Functions withWeights

Since data is becoming greatly large in the networks, the machine classification of the text data, is not easy under these computing circumstances. Though the k-nearest neighbor (kNN) classification is a simple and effective classification approach, the improving performance of the classifier is still attractive to cope with the high accuracy processing. In this paper, the kNN is improved by applying the different distance functions with weights to measure data from the multi-view points. Then, the weights for the optimization are computed by the genetic algorithms. After the learning of the trained data, the unknown data is classified by combining the multiple distance functions and ensemble computations of the kNN. In this paper we present a new approach to combine multiple kNN classifiers based on different distance functions, which improve the performance of the k-nearest neighbor method. The proposed combining algorithm shows the higher generalization accuracy when compared to other conventional learning algorithms

Agent Model for Dynamically Changing Plan in π-Calculus

Abstract The authors propose a new model for the agent system and a new language. First, they develop a new model to use a new language developed for describing agent plans based on 7r-calculus, called PDL (Plan Description Language). The plans described in PDL can be changed dynamically while it is executing, because 7r-calculus provides dynamically changing structures. By using this property, agents can, in executing their plans, change those plans to adapt to the environment around them. This property is very important to agents, and is called reflection. Second, the authors implement a system. In order to do so, they propose a primitive language, called PiL (Pi-calculus Language). PiL is used on a computer more easily than the mathematical notations of 7r-calculus. Finally, they show, by using this system, that their proposition is very useful in a dynamically changing environment.

Using an Artificial Neural Network for Predicting Embedded Software Development Effort

In this paper, we establish an effort prediction modelusing an artificial neural network (ANN) for complementingmissing values. We add missing values to the data viacollaborative filtering using the method of Tsunoda et al.’smethod[14]. In addition, we perform an evaluation experimentto compare the accuracy of the ANN model with that ofthe MRA model using Welch’s t-test[16]. The results showthat the ANN model is more accurate than the MRA model,since the mean errors of the ANN are statistically significantlylower.

Error Estimation Models Integrating Previous Models and Using Artificial Neural Networks for Embedded Software Development Projects

In an earlier paper, we established 9 models for estimating errors in a new project. In this paper, we integrate the 9 models into 5 by investigating similarities among the models. In addition, we establish a new model using an artificial neural network (ANN). It is becoming increasingly important for software-development corporations to ascertain how to develop software efficiently, whilst guaranteeing delivery time and quality, and keeping development costs low. Estimating the manpower required by new projects and guaranteeing the quality of software are particularly important, because the estimation relates directly to costs while the quality reflects on the reliability of the corporations. In the field of embedded software, development techniques, management techniques, tools, testing techniques, reuse techniques, real-time operating systems and so on, have already been studied. However, there is little research on the relationship between the scale of the development and the number of errors using data accumulated from past projects. Hence, we integrate the previous models and establish a new model using an artificial neural network (ANN). We also compare the accuracy of the ANN model and the regression analysis models. The results of these comparisons indicate that the ANN model is more accurate than any of the 5 integrated models.

Improving Accuracy of Multiple Regression Analysis for Effort Prediction Model

In this paper, we outline the effort prediction model and the evaluation experiment. In addition we explore the parameters in the model. The model predicts effort of embedded software developments via multiple regression analysis using the collaborative filtering. Because companies, recently, focus on methods to predict effort of projects, which prevent project failures such as exceeding deadline and cost, due to more complex embedded software, which brings the evolution of the performance and function enhancement. In the model, we have fixed two parameters named k and ampmax, which would influence the accuracy of predicting effort. Hence, we investigate a tendency of them in the model and find the optimum value

Improving Accuracy of an Artificial Neural Network Model to Predict Effort and Errors in Embedded Software Development Projects

In this paper we propose a method for reducing the margin of error in effort and error prediction models for embedded software development projects using artificial neural networks(ANNs). In addition, we perform an evaluation experiment that uses Welch’s t-test to compare the accuracy of the proposed ANN method with that of our original ANN model. The results show that the proposed ANN model is more accurate than the original one in predicting the number of errors for new projects, since the means of the errors in the proposed ANN are statistically significantly lower.

Studies on Project Management Models for Embedded Software Development Projects

In a corporation or the division of a corporation where software is being developed, it is becoming very important to develop software efficiently while guaranteeing the quality, limiting the cost, and maintaining the development schedule. Therefore, the corporation and the division of the corporation that develop software are implementing various improvement methods, including process improvement. In this study we have analyzed data from a software development project and studied how to determine which software development project will fail because it takes more manpower that originally estimated. In addition, we have implemented project-management support tools by combining the project-monitoring and managing tools that are already in use. We have also developed a new model that uses the statistical method, by reviewing the tools that are already used to estimate manpower for new projects

Effort prediction model using similarity for embedded software development

In this paper, we propose an effort prediction model in which data including missing values is complemented by using the collaborative filtering [1, 2, 3] and the effort of projects is derived from a multiple regression analysis [4, 5] using the data. Because companies, recently, focus on methods to predict effort of projects, which prevent project failures such as exceeding deadline and cost, due to more complex embedded software, which brings the evolution of the performance and function enhancement [6, 7, 8]. Moreover, we conduct the evaluation experiment that compared the accuracy of our method with other two methods according to five criteria to confirm their accuracy. The results of the experiment shows that our method gives predictions the best in the five evaluation criteria.

Classification on nonlinear mapping of reducts based on nearest neighbor relation

Dimension reduction of data is an important theme in the data processing and on the web to represent and manipulate higher dimensional data. Rough set is fundamental and useful to process higher dimensional data. Reduct in the rough set is a minimal subset of features, which has the same discernible power as the entire features in the higher dimensional scheme. It is shown that nearest neighbor relation with minimal distance introduced here has a basic information for classification. In this paper, a new reduct generation method based on the nearest neighbor relation with minimal distance is proposed. To improve the classification accuracy of reducts, we develop a nonlinear mapping method on the nearest neighbor relation, which makes vector data relation among neighbor data and preserves data ordering.

Clustering and Analyzing Embedded Software Development Projects Data Using Self-Organizing Maps

In this paper, we cluster and analyze data from the past embedded software development projects using self-organizing maps (SOMs)[9] that are a type of artificial neural networks that rely on unsupervised learning. The purpose of the clustering and analysis is to improve the accuracy of predicting the number of errors. A SOMproduces a low-dimensional, discretized representation of the input space of training samples; these representations are called maps. SOMs are useful for visualizing low-dimensional views of high-dimensional data, a multidimensional scaling technique. The advantages of SOMs for statistical applications are as follows: (1) data visualization, (2) information processing on association and recollection, (3) summarizing large-scale data, and (4) creating nonlinear models. To verify our approach, we perform an evaluation experiment that compares SOM classification to product type classification using Welch’s t-test for Akaike’s Information Criterion (AIC). The results indicate that the SOM classification method is more contributive than product type classification in creating estimation models, because the mean AIC of SOM classification is statistically significantly lower.