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.
1295488
Medical data mining using evolutionary computation
In this paper, we introduce a system for discovering medical knowledge by learning Bayesian networks and rules. Evolutionary computation is used as the search algorithm. The Bayesian networks can provide an overall structure of the relationships among the attributes. The rules can capture detailed and interesting patterns in the database. The system is applied to real-life medical databases for limb fracture and scoliosis. The knowledge discovered provides insights to and allows better understanding of these two medical domains.
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.
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. >
8712610
A self-organizing map with expanding force for data clustering and visualization
The self-organizing map (SOM) is a powerful tool in the exploratory phase of data mining. However, due to the dimensional conflict, neighborhood preservation cannot always lead to perfect topology preservation. In this paper we establish an expanding SOM (ESOM) to detect and preserve better topology correspondence between the two spaces. Our experiment results demonstrate that the ESOM constructs better mappings than the classic SOM in terms of both topological and quantization errors. Furthermore, clustering results generated by the ESOM are more accurate than those of the SOM.
2011139
Discovering knowledge from noisy databases using genetic programming
In data mining, we emphasize the need for learning from huge, incomplete and imperfect data sets (Fayyad et al. 1996, Frawley et al. 1991, Piatetsky-Shapiro and Frawley, 1991). To handle noise in the problem domain, existing learning systems avoid overfitting the imperfect training examples by excluding insignificant patterns. The problem is that these systems use a limiting attribute-value language for representing the training examples and the induced knowledge. Moreover, some important patterns are ignored because they are statistically insignificant. In this paper, we present a framework that combines Genetic Programming (Koza 1992; 1994) and Inductive Logic Programming (Muggleton, 1992) to induce knowledge represented in various knowledge representation formalisms from noisy databases. The framework is based on a formalism of logic grammars and it can specify the search space declaratively. An implementation of the framework, LOGENPRO (The Logic grammar based GENetic PROgramming system), has been developed. The performance of LOGENPRO is evaluated on the chess endgame domain. We compare LOGENPRO with FOIL and other learning systems in detail and find its performance is significantly better than that of the others. This result indicates that the Darwinian principle of natural selection is a plausible noise handling method which can avoid overfitting and identify important patterns at the same time. Moreover, the system is applied to one real-life medical database. The knowledge discovered provides insights to and allows better understanding of the medical domains. Area: Data mining, Genetic Programming, Evolutionary Computation, Rule Learning
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.
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
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.