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Using sensitivity analysis and visualization techniques to open black box data ...

In this paper, we propose a new visualization approach based on a Sen- sitivity Analysis (SA) to extract human understandable knowledge from su- pervised learning black box data mining models, such as Neural Networks (NN), Support Vector Machines (SVM) and ensembles, including Random Forests (RF). Five SA methods (three of which are purely new) and four mea- sures of input importance (one novel) are presented. ...

Forecasting seasonal time series with computational intelligence: on recent met...

Accurate time series forecasting is a key issue to support individual and or- ganizational decision making. In this paper, we introduce novel methods for multi-step seasonal time series forecasting. All the presented methods stem from computational intelligence techniques: evolutionary artificial neu- ral networks, support vector machines and genuine linguistic fuzzy rules. Performance of the suggested methods ...

Email spam detection : a symbiotic feature selection approach fostered by evolu...

Post-print version (prior to journal publication) ; The electronic mail (email) is nowadays an essential communication service being widely used by most Internet users. One of the main problems affecting this service is the proliferation of unsolicited messages (usually denoted by spam) which, despite the efforts made by the research community, still remains as an inherent problem affecting this Internet servi...

Time series forecasting using a weighted cross-validation evolutionary artifici...

The ability to forecast the future based on past data is a key tool to support individual and organizational decision making. In particular, the goal of Time Series Forecasting (TSF) is to predict the behavior of complex systems by looking only at past patterns of the same phenomenon. In recent years, several works in the literature have adopted Evolutionary Artificial Neural Networks (EANNs) for TSF. In this w...

Editorial [to] Knowledge discovery and business intelligence

Some experiments on modeling stock market behavior using investor sentiment ana...

The analysis of microblogging data related with stock mar- kets can reveal relevant new signals of investor sentiment and attention. It may also provide sentiment and attention indicators in a more rapid and cost-effective manner than other sources. In this study, we created several indicators using Twitter data and investigated their value when model- ing relevant stock market variables, namely returns, tradin...

Multiscale Internet traffic forecasting using neural networks and time series m...

This article presents three methods to forecast accurately the amount of traffic in TCP=IP based networks: a novel neural network ensemble approach and two important adapted time series methods (ARIMA and Holt-Winters). In order to assess their accuracy, several experiments were held using real-world data from two large Internet service providers. In addition, different time scales (5min, 1h and 1 day) and dist...

Using data mining to study the impact of topology characteristics on the perfor...

This paper quantifies the impact of topological characteristics on the performance of single radio multichannel IEEE802.11 mesh networks. Topological characteristics are the number of nodes per subnetwork, the hop count, the neighbor node density, the hidden nodes, the number of nodes in the neighborhood of the gateway, and the hidden nodes in the neighborhood of the gateway. Network performance metrics are thr...

Application of a sensitivity analysis procedure to interpret uniaxial compressi...

Jet Grouting (JG) technology, one of the most efficient soft soils improvement methods, has been widely applied in important geotechnical works due to its versatility. However, there is still an important limitation to overcome related with the absence of rational approaches for its design. In the present work, three different Data Mining (DM) techniques, i.e., Artificial Neuronal Networks (ANN), Support Vector...

Evolutionary symbiotic feature selection for email spam detection

This work presents a symbiotic filtering approach enabling the exchange of relevant word features among different users in order to improve local anti-spam filters. The local spam filtering is based on a Content- Based Filtering strategy, where word frequencies are fed into a Naive Bayes learner. Several Evolutionary A l gori thms are expl ored f or f eature sel ecti on, i ncl udi ng the proposed symbi oti c ex...