MAT: a Mobile Agent System for supporting autonomous mobile agents

Abstrac

Identifying Potential synthesis cases in distributed expert systems: a fuzzy logic approach

Abstrac

A divide-and-conquer based ensemble classifier learning by means of many-objective optimization

IEEE Divide-and-conquer based methods are quite successful across various problems from different disciplines. These methods divide a complex task into multiple simple tasks and solve them collectively. This paper presents a divide-and-conquer based hierarchical optimization framework for ensemble classifier learning. The optimization framework includes a search space creation process (called Data Training Environments (DTE)) that divides the data into multiple clusters, and then trains a set of heterogeneous base classifiers with the DTEs. The classifiers are then combined to form an optimal ensemble, by finding the fittest ones using many-objective optimization. The many-objective optimization algorithm considers each class accuracy as a separate objective and maximizes the class accuracies. An additional objective is also taken into account by maximizing the ensemble size. Since the partitioning of data creates diversity within the pool of classifiers, class accuracy trade-off among the classifiers is observed. As a result, increasing the number of classifiers also increases the diversity within the ensemble. In order to tackle the optimization, a specialized many-objective optimization algorithm based on decomposition is proposed. Since ensemble classifier learning can be regarded as an NP-hard problem, the proposed optimization algorithm, instead identifies the optimal ensemble using a divide-and-conquer rule-based chromosome encoding. Moreover, with the involvement of individual class accuracy in the objectives, the performance does not get biased towards any majority class. The proposed framework is experimented with 24 benchmark datasets obtained from the UCI machine learning repository and compared with the existing approaches. The experimental results show better classification accuracy with the proposed framework in comparison with the recent ensemble classifiers

Optimizing configuration of neural ensemble network for breast cancer diagnosis

Determining the best values for the parameters of a classifier is a challenge. This challenge is compounded for ensembles. This research evaluates the number of neurons for candidate networks and the number of committee members in our work on variable neural classifiers for breast cancer diagnosis. The evaluation reveals that good neural network accuracy can be achieved with a small number of neurons in the hidden layer and three committee members in the ensemble. The proposed methodology is tested on two benchmark databases achieving 99% classification accuracy

Optimizing configuration of neural ensemble network for breast cancer diagnosis

Determining the best values for the parameters of a classifier is a challenge. This challenge is compounded for ensembles. This research evaluates the number of neurons for candidate networks and the number of committee members in our work on variable neural classifiers for breast cancer diagnosis. The evaluation reveals that good neural network accuracy can be achieved with a small number of neurons in the hidden layer and three committee members in the ensemble. The proposed methodology is tested on two benchmark databases achieving 99% classification accuracy

A computational strategy for information integration in meta-search agent systems

When several information search agents work together for a user's query, each will obtain a result. How to integrate these multiple results so as to provide the final result to the user has become an important issue in both distributed information retrieval and the application of multi-agent systems. In this paper, we present a computational strategy for information integration, which is based on ranking orders of multiple results, similarities of search agents, and reliabilities of results from different search agents. This strategy is essentially a framework that can be combined with any other method to form a powerful integration strategy in distributed information retrieval

An incremental ensemble classifier learning by means of a rule-based accuracy and diversity comparison

In this paper, we propose an incremental ensemble classifier learning method. In the proposed method, a set of accurate and diverse classifiers are generated and added to the ensemble by means of accuracy and diversity comparison. The selection of classifiers in ensemble starts with a layer (where data is partitioned into any given number of clusters and fed to a set of base classifiers) and then continues to improve the bias-variance (i.e., accuracy and diversity). Optimal ensemble classifier selection is done through accuracy-precedence-diversity comparison, i.e., a model with better accuracy is preferred but in the case of models with the same accuracy, better diversity is preferred. The comparison is made on the class decomposed accuracies (i.e., all class accuracies are decomposed to a scalar value). A non-identical set of base classifiers is trained on the clusters of data in a layer and the center of the cluster is recorded as an identifier to the corresponding base classifiers set. Decisions from multiple base classifiers are fused to an ensemble class output using majority voting for each pattern and finally the decisions across multiple layers are combined using majority voting. The proposed method is evaluated on UCI benchmark datasets and compared with the recently proposed ensemble classifiers including the Bagging and Boosting. Through comparison, we demonstrate that the proposed method improves the performance of the base classifiers and performs better than the existing ensemble methods

Identifying and breaking necessary constraints to web-based metacomputing

Metacomputing aims at using the computing power of computers linked by networks for various applications, which used to be tackled by dedicated supercomputers a few years ago. Web-based metacomputing offers a cheap and efficient solution for parallel and distributed computing by exploiting idle cycles from the Internet. Research literatures have been published to report the state of the art of metacomputing by addressing the developed prototypes – architecture, support methodologies and technologies, programming APIs, example benchmarks and experimental performances. This paper tries to answer the question: why the metacomputing system is undesirable in performance, applicability and reliability. In this paper, the system’s components are modelled as three roles: Client, Worker and Manager. Their performances are individually investigated in terms of the Theory of Constraint: Objectives, Requirements, Prerequisites, Conflicts and Injections for identifying core problems and exploring efficient solutions. The main contribution of this paper is to be a very first literature as conceptual guidelines for design and optimisation of metacomputing systems

Image descriptor : a genetic programming approach to multiclass texture classification

Texture classification is an essential task in computer vision that aims at grouping instances that have a similar repetitive pattern into one group. Detecting texture primitives can be used to discriminate between materials of different types. The process of detecting prominent features from the texture instances represents a cornerstone step in texture classification. Moreover, building a good model using a few training instances is difficult. In this study, a genetic programming (GP) descriptor is proposed for the task of multiclass texture classification. The proposed method synthesises a set of mathematical formulas relying on the raw pixel values and a sliding window of a predetermined size. Furthermore, only two instances per classare used to automatically evolve a descriptor that has the potential to effectively discriminate between instances of different textures using a simple instance-based classifier to perform the classification task. The performance of the proposed approach is examined using two widely-used data sets, and compared with two GP-based and nine well-known non-GP methods. Furthermore, three hand-crafted domain-expert designed feature extraction methods have been used with the non-GP methods to examine the effectiveness of the proposed method. The results show that the proposed method has significantly outperformed all these other methods on both data sets, and the new method evolves a descriptor that is capable of achieving significantly better performance compared to hand-crafted features

A fuzzy logic based strategy for information integration in meta-search agent systems

When several information search agents work together for a user's query, each will obtain a result. The structures and relations of the search agents of the system may influence the accuracy and correctness of the result. How to integrate multiple results and provide an accurate final result to the user has become an important issue in both distributed information retrieval and the application of multi-agent systems. In this paper, we present a fuzzy logic based strategy for information integration, which is based on ranking orders of multiple results, similarities of search agents, and reliabilities of results from different search agents. This strategy is essentially a framework that can be combined with any other method to form a powerful integration strategy in distributed information retrieval