Juan F. Gálvez

Rough sets for spam filtering: Selecting appropriate decision rules for boundary e-mail classification

Nowadays, spam represents an extensive subset of the information delivered through Internet involving all unsolicited and disturbing communications received while using different services including e-mail, weblogs and forums. In this context, this paper reviews and brings together previous approaches and novel alternatives for applying rough set (RS) theory to the spam filtering domain by defining three different rule execution schemes: MFD (most frequent decision), LNO (largest number of objects) and LTS (largest total strength). With the goal of correctly assessing the suitability of the proposed algorithms, we specifically address and analyse significant questions for appropriate model validation like corpus selection, preprocessing and representational issues, as well as different specific benchmarking measures. From the experiments carried out using several execution schemes for selecting appropriate decision rules generated by rough sets, we conclude that the proposed algorithms can outperform other well-known anti-spam filtering techniques such as support vector machines (SVM), Adaboost and different types of Bayes classifiers.

A hybrid artificial intelligence model for river flow forecasting

Abstract A hybrid hydrologic estimation model is presented with the aim of performing accurate river flow forecasts without the need of using prior knowledge from the experts in the field. The problem of predicting stream flows is a non-trivial task because the various physical mechanisms governing the river flow dynamics act on a wide range of temporal and spatial scales and almost all the mechanisms involved in the river flow process present some degree of nonlinearity. The proposed system incorporates both statistical and artificial intelligence techniques used at different stages of the reasoning cycle in order to calculate the mean daily water volume forecast of the Salvajina reservoir inflow located at the Department of Cauca, Colombia. The accuracy of the proposed model is compared against other well-known artificial intelligence techniques and several statistical tools previously applied in time series forecasting. The results obtained from the experiments carried out using real data from years 1950 to 2006 demonstrate the superiority of the hybrid system.

Classification of honeybee pollen using a multiscale texture filtering scheme

Abstract.People are interested in the composition of honeybee pollen due to its nutritional value and therapeutic benefits. Its palynological composition depends on the local flora surrounding the beehive, and its identification is currently done manually using optical microscopy. This procedure is tedious and expensive in systematic application and is unable to automatically separate pollen loads of different species of plants. We present an automatic methodology to discriminate pollen loads based on texture image classification. Texture features are generated using a multiscale filtering scheme. A statistical evaluation of the algorithm is provided and discussed.

Esters flash point prediction using artificial neural networks

In this article, an artificial neural network to predict the flash point of 95 esters was implemented. Four variables were used for its development. A neural network with 4‐5‐8‐5‐1 topology was encountered to gain the best agreement of the experimental results with those predicted (square correlation coefficient (R2) and root mean square error were 0.99 and 5.46 K for the training phase and 0.96 and 13.02 K for the testing set).

An Application for Knowledge Discovery Based on a Revision of VPRS Model

In this paper, we present a particular study of the negative factors that affect the performance of university students. The analysis is carried out using the CAI (Conjuntos Aproximados con Incertidumbre) model that is a new revision of the VPRS (Variable Precision Rough Set) model. The major contribution of the CAI model is the approximate equality among knowledge bases. This concept joined with the revision of the process of knowledge reduction (concerning both attributes and categories), allow a significant reduction in the number of generated rules and the number or attributes per rule as it is showed in the case of study.

Using variable precision rough set for selection and classification of biological knowledge integrated in DNA gene expression.

DNA microarrays have contributed to the exponential growth of genomic and experimental data in the last decade. This large amount of gene expression data has been used by researchers seeking diagnosis of diseases like cancer using machine learning methods. In turn, explicit biological knowledge about gene functions has also grown tremendously over the last decade. This work integrates explicit biological knowledge, provided as gene sets, into the classication process by means of Variable Precision Rough Set Theory (VPRS). The proposed model is able to highlight which part of the provided biological knowledge has been important for classification. This paper presents a novel model for microarray data classification which is able to incorporate prior biological knowledge in the form of gene sets. Based on this knowledge, we transform the input microarray data into supergenes, and then we apply rough set theory to select the most promising supergenes and to derive a set of easy interpretable classification rules. The proposed model is evaluated over three breast cancer microarrays datasets obtaining successful results compared to classical classification techniques. The experimental results shows that there are not significant differences between our model and classical techniques but it is able to provide a biological-interpretable explanation of how it classifies new samples.

Artificial neural networks: a promising tool to evaluate the authenticity of wine Redes neuronales: una herramienta prometedora para evaluar la autenticidad del vino

Artificial Neural Networks (ANNs) have demonstrated to be a good tool to characterise, model and predict a great quantity of non-linear processes. In this article, we have used ANNs in the classification of different wine-making processes of the variety Vinhão (Vitis vinifera) for crops between the years 2000 and 2004. After being trained employing the data corresponding to years from 2000 to 2004, the ANNs demonstrated a root mean square error (RMSE) index between the real data and the calculated ones always lower than 0.14. Furthermore, their operation has been verified by using the previously reserved data of 10 famous wines. As a result, a RMSE index between observed and calculated data always lower than 0.17 was obtained for all of them, confirming the capacity of the ANN as a model of prediction of wine processes for this variety.

Determine the Composition of Honeybee Pollen by Texture Classification

Humans are interested in the knowledge of honeybee pollen composition, which depends on the local flora surrounding the beehive, due to their nutritional value and therapeutical benefits. Currently, pollen composition is manually determined by an expert palynologist counting the proportion of pollen types analyzing the pollen of the hive with an optical microscopy. This procedure is tedious and expensive for its systematic application. We present an automatic methodology to discriminate pollen loads of various genus based on texture classification. The method consists of three steps: after selection non-blurred regions of interest (ROIs) in the original image, a texture feature vector for each ROI is calculated, which is used to discriminate between pollen types. An statistical evaluation of the algorithm is provided and discussed.

Biological Knowledge Integration in DNA Microarray Gene Expression Classification Based on Rough Set Theory

DNA microarrays have contributed to the exponential growth of genetic data from years. One of the possible applications of this large amount of gene expression data diagnosis of diseases like cancer using classification methods. In turn, explicit biological knowledge about gene functions has also grown tremendously over the last decade. This work integrates explicit biological knowledge in classification process using Rough Set Theory, making it more effective. In addition, the proposed model is able to indicate which part of biological knowledge has been used building the model and classifing new samples.

Active Safety Evaluation in Car-to-Car Networks

Currently, the main purpose in car-to-car networks is to improve communication performance. In order to prove this, we need a tool to evaluate statistical values such as the number of vehicles that have received the information, time of reception of the message, etc. In this study, we demonstrate how it is possible to simulate real scenarios with car-to-car technology and evaluate all traffic parameters obtained in simulations with hundreds of vehicles that receive alert signals for specific events. We will simulate one traffic accident both in a highway and in a urban scenario with different traffic patterns and we will analyze how data are received by the cars that are located in the coverage range. Thus, we can know the topology deficiencies in certain situations and in which situations, for example, it is possible to provide Road Side Units (RSU) or Public Hot Spot (PHS) units to perform network messages.