Kenneth J. Mackin

Artificial neural network ensemble-based land-cover classifiers using MODIS data

Terra and Aqua, two satellites launched by the NASA-centered International Earth Observing System project, house MODIS (moderate resolution imaging spectroradiometer) sensors. Moderate-resolution remote sensing allows the quantifying of land-surface type and extent, which can be used to monitor changes in land cover and land use for extended periods of time. In this article, we propose land-surface classification by applying an ensemble technique based on fault masking among individual classifiers in N-version programming. An N-version programming ensemble of artificial neural networks is created, in which the majority vote result is used to predict land-surface cover from MODIS data. It is shown by experiment that an N-version programming ensemble of neural networks greatly improves the classification error rate of land-cover type.

Knowledge Discovery and Data Mining in Medicine

Medical databases store diagnostic information based on patients’ medical records. Because of deficits in patients’ medical records, medical databases do not provide all the required information for learning algorithms. Moreover, we may meet some contradictory cases, in which the pattern of input signals is the same, but the pattern of output signals is different. Learning algorithms cannot correctly classify such cases. Even medical doctors require more information to make the final diagnosis. In this chapter, we describe three methods of classifying medical databases based on neural networks and genetic programming (GP). To verify the effectiveness of our proposed methods, we apply them to real medical databases and prove their high classification capability. We also introduce techniques for extracting If-Then rules from the trained networks.

Development and evaluation of satellite image data analysis infrastructure

Tokyo University of Information Sciences (TUIS) receives moderate resolution imaging spectroradiometer (MODIS) data, and provides the processed data to universities and research institutes as part of the academic frontier project. One of the major fields of research using MODIS data is the analysis of changes in the environment. We are currently developing applications to analyze environmental changes. These applications run on our satellite image data analysis system, which is implemented in a parallel distributed system and a database server. When using satellite data, one common problem is the interference of clouds. In order to remove this interference, the standard solution is to create composite data of the same regions during a selected time span, and to patch together data which are not covered by clouds to create a clear image. We introduced a piece-processing algorithm which separates one set of satellite image data into many small pieces of image data, making it quicker and easier to analyze and process the time-series satellite data. In this research, we implemented the pieceprocessing and composite-processing algorithms in order to increase the speed of analysis within the satellite image database. We tested the proposed processing and verified its effectiveness for target applications.

Artificial neural networks paddy-field classifier using spatiotemporal remote sensing data

Monitoring changes in a paddy-field area is important since rice is a staple food and paddy agriculture is a major cropping system in Asia. For monitoring changes in land surface, various applications using different satellites have been researched in the field of remote sensing. However, monitoring a paddy-field area with remote sensing is difficult owing to the temporal changes in the land surface, and the differences in the spatiotemporal characteristics in countries and regions. In this article, we used an artificial neural network to classify paddy-field areas using moderate resolution sensor data that includes spatiotemporal information. Our aim is to automatically generate a paddy-field classifier in order to create localized classifiers for each country and region.

Applying Brightness Information in Satellite Image Data Search using Distributed Genetic Algorithm

Tokyo University of Information Sciences maintains and distributes MODIS (Moderate Resolution Imaging Spectroradiometer) satellite data as part of the research output for Frontier project. An intelligent image search system is being developed as part of the project, in order to retrieve requested images such as matching images patterns or forest and field fires extraction. The intelligent image search system applies genetic algorithm (GA) in the search algorithm. When searching for a target image area within the MODIS image database, it is possible that the search algorithm cannot match the optimal location when the brightness of the search image data and MODIS data image are very different. In order to solve this problem, we extended the search algorithm by implementing brightness information in the GA chromosome, so that brightness is adjusted within the GA image search. Further, we implemented the image search as distributed genetic algorithm search over a PC cluster network, in order to increase the search speed within the satellite image database. We tested the proposed system and verified the effectiveness of distributed genetic algorithm for the distributed MODIS satellite database search process.

Cluster ensemble in adaptive tree structured clustering

Adaptive tree structured clustering (ATSC) is our proposed divisive hierarchical clustering method that recursively divides a data set into two subsets using self-organising feature map (SOM). In each partition, after the data set is quantised by SOM, the quantised data is divided using agglomerative hierarchical clustering. ATSC can divide the data sets regardless of data size in feasible time. On the other hand the number of cluster and the members of each cluster are not universal in each run. This non-universality is fundamental problem in the other divisive hierarchical clustering and partitioned clustering. In this paper, we apply cluster ensemble to each data partition of ATSC in order to improve universality. Cluster ensemble is a framework by using multiple learners for improving universality. From the computer simulation, we showed that the proposed method is effective for improving universality. Moreover, the accuracy was improved by solving the non-universality of each partition.

The medical diagnostic support system using extended Rough Neural Network and Multiagent

Multiagent technologies enable us to explore their sociological and psychological foundations. Amedical dignostic support system is built using this. Moreover, We think that the data inputted can acquire higher diagnostic accuracy by sorting out using a determination table. In this paper, the recurrence diagnostic system of cancer is built and the output error of Multiagents learning method into the usual Neural Network and a Rough Neural Network and Genetic Programming be compared. The data of the prostates cancer offered by the medical institution and a renal cancer was used for verification of a system.

Multiagent communication combining genetic programming and pheromone communication

Multiagent systems, in which independent software agents interact with each other to achieve common goals, complete concurrent distributed tasks under autonomous control. Agent Communication has been shown to be an important factor in coordinating efficient group behavior in agents. Most researches on training or evolving group behavior in multiagent systems used predefined agent communication protocols. Designing agent communication becomes a complex problem in dynamic and large‐scale systems. In order to solve this problem, in this paper we propose a new application of existing training methods. By applying Genetic Programming techniques, namely Automatically Defined Function Genetic Programming (ADF‐GP), in combination with pheromone communication features, we allowed the agent system to autonomously learn effective agent communication messaging for coordinated group behavior. A software simulation of a multiagent transaction system aiming at e‐commerce usage will be used to observe the effectiveness of the proposed method in the targeted environment. Using the proposed method, automatic training of a compact and efficient agent communication protocol for the multiagent system was observed.

Evolving intelligent multiagent systems using unsupervised agent communication and behavior training

Multiagent systems in which independent software agents interact with each other to achieve common goals, complete distributed tasks concurrently under autonomous control. Agent communication has been shown to be an important factor in coordinating efficient group behavior in agents. Most research on training or evolving group behavior in multiagent systems used predefined agent communication protocols. Designing agent communication becomes a complex problem in dynamic and large-scale systems. In order to solve this problem, in our previous research we proposed a method applying genetic programming techniques, in particular Automatically Defined Function Genetic Programming (ADF-GP) (K. Mackin and E. Tazaki, 1999), to allow agents to autonomously learn effective agent communication messaging. For this research we take this approach further and combine training of the agent behavior as well as the communication protocol. By training both behavior and communication we expect to further optimize the system performance. A software simulation of a multiagent transaction system is used to observe the effectiveness of the proposed method.

Unsupervised training of multiobjective agent communication using genetic programming

Multiagent systems, in which independent software agents interact with each other to achieve common goals, complete distributed tasks concurrently under autonomous control. Agent communication has been shown to be an important factor in coordinating efficient group behavior in agents. Most research on training or evolving group behavior in multiagent systems used predefined agent communication protocols. Designing agent communication becomes a complex problem in dynamic and large-scale systems. The problem is further complicated in a multiobjective scenario. In order to solve this problem, in our previous research we had proposed a method applying genetic programming techniques, in particular automatically defined function genetic programming (ADF-GP), to allow agents to autonomously learn effective agent communication messaging. For this research we take this approach further and combine multiobjective genetic programming in order to adapt the system to a multiobjective environment. In the proposed method separate agent communication protocols are trained for each objective. A software simulation of a multiagent transaction system is used to observe the effectiveness of the proposed method in multiobjective environments.