Vitorino Ramos

Web usage mining using artificial ant colony clustering and linear genetic programming

The rapid e-commerce growth has made both business community and customers face a new situation. Due to intense competition on the one hand and the customers option to choose from several alternatives, the business community has realized the necessity of intelligent marketing strategies and relationship management. Web usage mining attempts to discover useful knowledge from the secondary data obtained from the interactions of the users with the Web. Web usage mining has become very critical for effective Web site management, creating adaptive Web sites, business and support services, personalization, network traffic flow analysis and so on. The study of ant colonies behavior and their self-organizing capabilities is of interest to knowledge retrieval/management and decision support systems sciences, because it provides models of distributed adaptive organization, which are useful to solve difficult optimization, classification, and distributed control problems, among others [Ramos, V. et al. (2002), (2000)]. In this paper, we propose an ant clustering algorithm to discover Web usage patterns (data clusters) and a linear genetic programming approach to analyze the visitor trends. Empirical results clearly show that ant colony clustering performs well when compared to a self-organizing map (for clustering Web usage patterns) even though the performance accuracy is not that efficient when compared to evolutionary-fuzzy clustering (i-miner) [Abraham, A. (2003)] approach.

INTRUSION DETECTION SYSTEMS USING ADAPTIVE REGRESSION SPLINES

Past few years have witnessed a growing recognition of intelligent techniques for the construction of efficient and reliable intrusion detection systems. Due to increasing incidents of cyber attacks, building effective intrusion detection systems (IDS) are essential for protecting information systems security, and yet it remains an elusive goal and a great challenge. In this paper, we report a performance analysis between Multivariate Adaptive Regression Splines (MARS), neural networks and support vector machines. The MARS procedure builds flexible regression models by fitting separate splines to distinct intervals of the predictor variables. A brief comparison of different neural network learning algorithms is also given.

Varying the population size of artificial foraging swarms on time varying landscapes

In this paper we present a Swarm Search Algorithm with varying population of agents based on a previous model with fixed population which proved its effectiveness on several computation problems [6,7,8]. We will show that the variation of the population size provides the swarm with mechanisms that improves its self-adaptability and causes the emergence of a more robust self-organized behavior, resulting in a higher efficiency on searching peaks and valleys over dynamic search landscapes represented here by several three-dimensional mathematical functions that suddenly change over time.

Swarms on continuous data

While being it extremely important, many exploratory data analysis (EDA [J. Tukey (1977)]) systems have the inability to perform classification and visualization in a continuous basis or to self-organize new data-items into the older ones (even more into new labels if necessary), which can be crucial in KDD - knowledge discovery [U.M. Fayyad et al., (1996), (1992)], retrieval and data mining systems [S. Mitra et al., (2002), U.M. Fayyad et al., (1996)] (interactive and online forms of Web Applications are just one example). This disadvantage is also present in more recent approaches using self-organizing maps [R. Brits et al., (2001), H.P. Siemon et al., (1990)]. On the present work, and exploiting past successes in recently proposed stigmergic ant systems [V. Ramos et al., (2002)] a robust online classifier is presented, which produces class decisions on a continuous stream data, allowing for continuous mappings. Results show that increasingly better results are achieved, as demonstrated by other authors in different areas [V. Ramos et al., (1999), A. Lumini et al., (1997)].

Stock Market Prediction Using Multi Expression Programming

The use of intelligent systems for stock market predictions has been widely established. In this paper, we introduce a genetic programming technique (called multi-expression programming) for the prediction of two stock indices. The performance is then compared with an artificial neural network trained using Levenberg-Marquardt algorithm, support vector machine, Takagi-Sugeno Neuro-Fuzzy model and difference boosting neural network. We considered Nasdaq-100 index of Nasdaq Stock MarketSM and the S&P CNX NIFTY stock index as test data

ANTIDS: Self Orga nized Ant-Based C lustering Model for Intrusion Det ection System

Security of computers and the networks that connect them is increasingly becoming of great significance. Computer security is defined as the protection of computing systems against threats to confidentiality, integri ty, and availability. Due to the fact that it is almost difficult for a system administrator to recognize and manually intervene to stop an attack, there is an increasing re cognition that Int rusion Detection Systems (IDS) sh ould have a lot to earn on following i ts basic principle s on the behavior of complex natural systems, namely in what refers to self-organization, allowing for a real distributed and collective perception of this phenomena. Having that aim in mind, the present work p resents a self-organized ANT colony based Intrusion Detection System (ANTIDS) to detect intrusions in a network infrastru cture. The performance is compared among convention al soft computing paradigms like Dec ision Trees (DT), Support Vector Machines (SVM) and Linear Genetic Programming (LGP) to model fast, online and efficient intrusion detection systems.

The Biological Concept of Neoteny in Evolutionary Color Image Segmentation - Simple Experiments in Simple Non-memetic Genetic Algorithms

Neoteny, also spelled Paedomorphosis, can be defined in biological terms as the retention by an organism of juvenile or even larval traits into later life. In some species, all morphological development is retarded; the organism is juvenilized but sexually mature. Such shifts of reproductive capability would appear to have adaptive significance to organisms that exhibit it. In terms of evolutionary theory, the process of paedomorphosis suggests that larval stages and developmental phases of existing organisms may give rise, under certain circumstances, to wholly new organisms. Although the present work does not pretend to model or simulate the biological details of such a concept in any way, these ideas were incorporated by a rather simple abstract computational strategy, in order to allow (if possible) for faster convergence into simple nonmemetic Genetic Algorithms, i.e. without using local improvement procedures (e.g. via Baldwin or Lamarckian learning). As a case-study, the Genetic Algorithm was used for colour image segmentation purposes by using K-mean unsupervised clustering methods, namely for guiding the evolutionary algorithm in his search for finding the optimal or sub-optimal data partition. Average results suggest that the use of neotonic strategies by employing juvenile genotypes into the later generations and the use of linear-dynamic mutation rates instead of constant, can increase fitness values by 58% comparing to classical Genetic Algorithms, independently from the starting population characteristics on the search space.

KANTS: Artifical Ant System for Classification

This paper investigates a new model that takes advantage of the cooperative self-organization of Ant Algorithms to evolve a naturally inspired pattern recognition (and also clustering) method. The approach considers each data item as an ant that changes the environment as it moves through it. The algorithm is successfully applied to well-known classification problems and yields better results than some other classification approaches, like K-Nearest Neighbours and Neural Networks.

Computational chemotaxis in ants and bacteria over dynamic environments

Chemotaxis can be defined as an innate behavioural response by an organism to a directional stimulus, in which bacteria, and other single-cell or multicellular organisms direct their movements according to certain chemicals in their environment This is important for bacteria to find food (e.g., glucose) by swimming towards the highest concentration of food molecules, or to flee from poisons. Based on self-organized computational approaches and similar stigmergic concepts we derive a novel swarm intelligent algorithm. What strikes from these observations is that both eusocial insects as ant colonies and bacteria have similar natural mechanisms based on stigmergy in order to emerge coherent and sophisticated patterns of global collective behaviour. Keeping in mind the above characteristics we will present a simple model to tackle the collective adaptation of a social swarm based on real ant colony behaviors (SSA algorithm) for tracking extrema in dynamic environments and highly multimodal complex functions described in the well-know Dejong test suite. Then, for the purpose of comparison, a recent model of artificial bacterial foraging (BFOA algorithm) based on similar stigmergic features is described and analyzed. Final results indicate that the SSA collective intelligence is able to cope and quickly adapt to unforeseen situations even when over the same cooperative foraging period, the community is requested to deal with two different and contradictory purposes, while outperforming BFOA in adaptive speed. Results indicate that the present approach deals well in severe Dynamic Optimization problems.