7313400
Data mining of Bayesian networks using cooperative coevolution
This paper describes a novel data mining algorithm that employs cooperative coevolution and a hybrid approach to discover Bayesian networks from data. A Bayesian network is a graphical knowledge representation tool. However, learning Bayesian networks from data is a difficult problem. There are two different approaches to the network learning problem. The first one uses dependency analysis, while the second approach searches good network structures according to a metric. Unfortunately, the two approaches both have their own drawbacks. Thus, we propose a novel algorithm that combines the characteristics of these approaches to improve learning effectiveness and efficiency. The new learning algorithm consists of the conditional independence (CI) test and the search phases. In the CI test phase, dependency analysis is conducted to reduce the size of the search space. In the search phase, good Bayesian networks are generated by a cooperative coevolution genetic algorithm (GA). We conduct a number of experiments and compare the new algorithm with our previous algorithm, Minimum Description Length and Evolutionary Programming (MDLEP), which uses evolutionary programming (EP) for network learning. The results illustrate that the new algorithm has better performance. We apply the algorithm to a large real-world data set and compare the performance of the discovered Bayesian networks with that of the back-propagation neural networks and the logistic regression models. This study illustrates that the algorithm is a promising alternative to other data mining algorithms.
14153292
Scaling-Up Model-Based Clustering Algorithm by Working on Clustering Features
In this paper, we propose EMACF (Expectation-Maximization Algorithm for Clustering Features) to generate clusters from data summaries rather than data items directly. Incorporating with an adaptive grid-based data summarization procedure, we establish a scalable clustering algorithm: gEMACF. The experimental results show that gEMACF can generate more accurate results than other scalable clustering algorithms. The experimental results also indicate that gEMACF can run two order of magnitude faster than the traditional expectation-maximization algorithm with little loss of accuracy.
14418271
An efficient self-organizing map designed by genetic algorithms for the traveling salesman problem
As a typical combinatorial optimization problem, the traveling salesman problem (TSP) has attracted extensive research interest. In this paper, we develop a self-organizing map (SOM) with a novel learning rule. It is called the integrated SOM (ISOM) since its learning rule integrates the three learning mechanisms in the SOM literature. Within a single learning step, the excited neuron is first dragged toward the input city, then pushed to the convex hull of the TSP, and finally drawn toward the middle point of its two neighboring neurons. A genetic algorithm is successfully specified to determine the elaborate coordination among the three learning mechanisms as well as the suitable parameter setting. The evolved ISOM (eISOM) is examined on three sets of TSP to demonstrate its power and efficiency. The computation complexity of the eISOM is quadratic, which is comparable to other SOM-like neural networks. Moreover, the eISOM can generate more accurate solutions than several typical approaches for TSP including the SOM developed by Budinich, the expanding SOM, the convex elastic net, and the FLEXMAP algorithm. Though its solution accuracy is not yet comparable to some sophisticated heuristics, the eISOM is one of the most accurate neural networks for the TSP.
15319623
Inducing Logic Programs With Genetic Algorithms: The Genetic Logic Programming System
The Genetic Logic Programming System (GLPS) combines the learning power of genetic algorithms and the knowledge representation power of logic programming to overcome the shortcomings of existing paradigms. >
18850432
A hybrid approach to discover Bayesian networks from databases using evolutionary programming
Describes a data mining approach that employs evolutionary programming to discover knowledge represented in Bayesian networks. There are two different approaches to the network learning problem. The first one uses dependency analysis, while the second one searches good network structures according to a metric. Unfortunately, both approaches have their own drawbacks. Thus, we propose a hybrid algorithm of the two approaches, which consists of two phases, namely, the conditional independence test and the search phases. A new operator is introduced to further enhance the search efficiency. We conduct a number of experiments and compare the hybrid algorithm with our previous algorithm, MDLEP, which uses EP for network learning. The empirical results illustrate that the new approach has better performance. We apply the approach to data sets of direct marketing and compare the performance of the evolved Bayesian networks obtained by the new algorithm with the models generated by other methods. In the comparison, the induced Bayesian networks produced by the new algorithm outperform the other models.
17721223
Parallel evolutionary algorithms on graphics processing unit
Evolutionary algorithms (EAs) are effective and robust methods for solving many practical problems such as feature selection, electrical circuit synthesis, and data mining. However, they may execute for a long time for some difficult problems, because several fitness evaluations must be performed. A promising approach to overcome this limitation is to parallelize these algorithms. In this paper, we propose to implement a parallel EA on consumer-level graphics cards. We perform experiments to compare our parallel EA with an ordinary EA and demonstrate that the former is much more effective than the latter. Since consumer-level graphics cards are available in ubiquitous personal computers and these computers are easy to use and manage, more people are able to use our parallel algorithm to solve their problems encountered in real-world applications.
18042561
An Efficient Distributed Algorithm to Identify and Traceback DDoS Traffic
Distributed denial-of-service attack is one of the most pressing security problems that the Internet community needs to address. Two major requirements for effective traceback are (i) to quickly and accurately locate potential attackers and (ii) to filter attack packets so that a host can resume the normal service to legitimate clients. Most of the existing IP traceback techniques focus on tracking the location of attackers after-the-fact. In this work, we provide an efficient methodology for locating potential attackers who employ the flood-based attack. We propose a distributed algorithm so that a set of routers can correctly (in a distributed sense) gather statistics in a coordinated fashion and that a victim site can deduce the local traffic intensities of all these participating routers. We prove the correctness of our distributed algorithm, and given the collected statistics, we provide a method for the victim site to locate attackers who sent out dominating flows of packets. The proposed distributed traceback methodology can also complement and leverage on the existing ICMP traceback so that a more efficient and accurate traceback can be obtained. We carry out simulations to illustrate that the proposed methodology can locate the attackers in a short period of time. Moreover, the applications as well as the limitations of the proposed methodology are covered. We believe this work also provides the theoretical foundation on how to correctly and accurately perform distributed measurement and traffic estimation on the Internet.
9973907
Blind Phase-Amplitude Modulation Classification with Unknown Phase Offset
This paper first discusses the maximum likelihood (ML) classifier for automatic classification of digital modulations. The classifier is optimum for classification of phase-amplitude modulated signals under ideal environment. However, this is not the case in the presence of phase offset owing to inaccurate estimation. In this paper, we propose a novel non-coherent ML classifier to mitigate the effect phase offset. The non-coherent ML classifier adopts a pre-classification phase correction stage through a closed form estimator based on higher order statistics. Experimental results show improvement of classification accuracy at reasonable signal to noise ratio
29409552
Automatic digital modulation recognition using artificial neural network and genetic algorithm
Automatic recognition of digital modulation signals has seen increasing demand nowadays. The use of artificial neural networks for this purpose has been popular since the late 1990s. Here, we include a variety of modulation types for recognition, e.g. QAM16, V29, V32, QAM64 through the addition of a newly proposed statistical feature set. Two training algorithms for multi-layer perceptron (MLP) recogniser, namely Backpropagation with Momentum and Adaptive Learning Rate is investigated, while resilient backpropagation (RPROP) is proposed for this problem, are employed in this work. In particular, the RPROP algorithm is applied for the first time in this area. In conjunction with these algorithms, we use a separate data set as validation set during training cycle to improve generalisation. Genetic algorithm (GA) based feature selection is used to select the best feature subset from the combined statistical and spectral feature set. RPROP MLP recogniser achieves about 99% recognition performance on most SNR values with only six features selected using GA.