Exercícios resolvidos em Prolog sobre sistemas baseados em conhecimento

Apontamentos de apoio à disciplina de Sistemas Baseados em Conhecimento da Unidade de Ensino do Departamento de Sistemas de Informação da Escola de Engenharia da Universidade do Minho, Guimarães, Portugal.Apontamentos de ensino que explicam, através de exemplos, como resolver em Prolog diversos exercícios sobre Sistemas Baseados em Conhecimento: Regras de Produção, Estruturas Hierárquicas, Procura num Espaço de Soluções, Dependências Conceptuais e Programação Orientada para Padrões

Some scholarly communication guidelines

Scholarly communication describes the process of sharing and publishing of research findings. This report provides some useful guidelines for improving a key scholarly communication aspect: the writing of scientific documents (e.g. journal articles, conference papers, Doctor of Philosophy thesis). The goal is to have a written text to complement both a two hour seminar, given under the same subject and that was presented to Computer Science students, and the ``Scholarly Communication'' course unit, lectured for Information Systems students. For further reading purposes, this report includes an additional list of references related with other aspects of scholarly communication (e.g. designing scientific presentations)

Sensitivity analysis for time lag selection to forecast seasonal time series using neural networks and support vector machines

Multi-step ahead forecasting is an important issue for organizations, often used to assist in tactical decisions. Such forecasting can be achieved by adopting time series forecasting methods, such as the classical Holt-Winters (HW) that is quite popular for seasonal series. An alternative forecasting approach comes from the use of more flexible learning algorithms, such as Neural Networks (NN) and Support Vector Machines (SVM). This paper presents a simultaneous variable (i.e. time lag) and model selection algorithm for multi-step ahead forecasting using NN and SVM. Variable selection is based on a backward algorithm that is guided by a sensitivity analysis procedure, while model selection is achieved using a grid-search. Several experiments were devised by considering eight seasonal series and the forecasts were analyzed using two error criteria (i.e. SMAPE and MSE). Overall, competitive results were achieved when comparing the SVM and NN algorithms with HW.Fundação para a Ciência e Tecnologia (FCT) - Project PTDC/EIA/64541/2006

Artificial intelligence in knowledge management for Time Series Forecasting

Knowledge Management (KM) is a keen topic for an organization, in particular to those that have to deal with knowledge acquired from different sources, either from its own experiences or from that of others, to decide on the effective use of that knowledge to fulfill the goals of the organization. As representative examples of KM, one may have the object-oriented data bases, hypermedia or concept maps. On the other hand, techniques developed in Artificial Intelligence for knowledge representation and discovery may be of great use in KM; in particular, it seems natural to explore the potential of the organization past data to deal with management decisions of the present. One way is to use Time Series Forecasting (TSF), where forecasts are based on pattern recognition of past observations ordered in time. Traditional TSF methods, such as the Holt-Winters and the Box-Jenkins ones, are based on particular characteristics of the Time Series (TS), such as trend or seasonal effects. These methods work with well behaved TS, but present some drawbacks on TS with noise or some unknown nonlinear relations among the TS data. An alternative approach is the use of Artificial Neural Networks (ANNs), which present two main advantages: ANNs can extrapolate patterns from past data, even in TS with noise, and may adapt their behavior as new data comes in. A problem with the use of this approach is the search time for the best ANN architecture, which involves a large searching space, demanding a huge computational effort. Other aspect of concern is that of TS data filtering. Not all lags of the TS have the same influence over the forecast. Feeding the ANN with a big time window will slow the ANN forecasting efficiency. To solve these pitfalls, one may use random search, hill climbing or genetic procedures. The last ones are known to work well on problems of combinatorial nature, obtaining good solutions where other methods seem to fail. This paper presents an integrated approach for TSF: a set of rules will create the training cases, based on some lags of the TS; these rules and the ANN parameters will be encoded on the genetic chromosomes; finally, each ANN will be trained, leading to competition

Data mining via redes neuronais artificiais e máquinas de vectores de suporte

Este artigo pretende esclarecer quais as vantagens de dois modelos não lineares de Data Mining: as Redes Neuronais Artificiais (RNA) e as Máquinas de Vectores de Suporte (MVS). Em particular, pretende-se medir o desempenho destas técnicas quando aplicadas a tarefas de classificação e regressão, comparando-as com outras técnicas (i.e. Árvores de Decisão/Regressão). Assim, fez-se uma análise de ferramentas de software que implementam os modelos referidos, tendo-se escolhido duas aplicações de utilização livre (i.e. o ambiente R e o Weka) para conduzir as experiências efectuadas. Foram utilizados diversos problemas do mundo real, sendo que os resultados obtidos revelam que as MVS obtêm em geral melhores previsões, sendo seguidas pelas RNA.Este trabalho foi suportado pelo projecto FCT PTDC/EIA/64541/2006

Opening black box data mining models using sensitivity analysis

There are several supervised learning Data Mining (DM) methods, such as Neural Networks (NN), Support Vector Machines (SVM) and ensembles, that often attain high quality predictions, although the obtained models are difficult to inter- pret by humans. In this paper, we open these black box DM models by using a novel visualization approach that is based on a Sensitivity Analysis (SA) method. In particular, we propose a Global SA (GSA), which extends the applicability of previous SA methods (e.g. to classification tasks), and several visualization techniques (e.g. variable effect characteristic curve), for assessing input relevance and effects on the model’s responses. We show the GSA capabilities by conducting several experiments, using a NN ensemble and SVM model, in both synthetic and real-world datasets.(undefined

Using sensitivity analysis and visualization techniques to open black box data mining models

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. Also, the SA approach is adapted to handle discrete variables and to aggregate multiple sensitivity responses. Moreover, several visualizations for the SA results are introduced, such as input pair importance color matrix and variable effect characteristic surface. A wide range of experiments was performed in order to test the SA methods and measures by fitting four well-known models (NN, SVM, RF and decision trees) to synthetic datasets (five regression and five classification tasks). In addition, the visualization capabilities of the SA are demonstrated using four real-world datasets (e.g., bank direct marketing and white wine quality).The work of P. Cortez was funded by FEDER, through the program COMPETE and the Portuguese Foundation for Science and Technology (FCT), within the project FCOMP-01-0124-FEDER-022674. Also, the au- thors wish to thank the anonymous reviewers for their helpful comments

Fifth special issue on knowledge discovery and business intelligence

[extract] Artificial Intelligence (AI) is impacting our world. In the 1970s and 1980s, Expert Systems (ES) consisted of AI systems that included explicit knowledge, often represented in a symbolic form (e.g., by using the Prolog language), that was extracted from human experts.The work of P. Cortez was supported by FCT - Fundaçâo para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/202

Evolutionary support vector machines for time series forecasting

Abstract. Time Series Forecasting (TSF) uses past patterns of an event in order to predict its future values and is a key tool to support decision making. In the last decades, Computational Intelligence (CI) techniques, such as Artificial Neural Networks (ANN) and more recently Support Vector Machines (SVM), have been proposed for TSF. The accuracy of the best CI model is affected by both the selection of input time lags and the model’s hyperparameters. In this work, we propose a novel Evolutionary SVM (ESVM) approach for TSF based on the Estimation Distribution Algorithm to search for the best number of inputs and SVM hyperparameters. Several experiments were held, using a set of six time series from distinct real-world domains. Overall, the proposed ESVM is competitive when compared with an Evolutionary ANN (EANN) and the popular ARIMA methodology, while consuming less computational effort when compared with EANN.The research reported here has been supported by FEDER (program COMPETE and FCT) under project FCOMP-01-0124-FEDER-02267

Evolutionary optimization of sparsely connected and time-lagged neural networks for time series forecasting

Time Series Forecasting (TSF) is an important tool to support decision mak- ing (e.g., planning production resources). Artificial Neural Networks (ANN) are innate candidates for TSF due to advantages such as nonlinear learn- ing and noise tolerance. However, the search for the best model is a complex task that highly affects the forecasting performance. In this work, we propose two novel Evolutionary Artificial Neural Networks (EANN) approaches for TSF based on an Estimation Distribution Algorithm (EDA) search engine. The first new approach consist of Sparsely connected Evolutionary ANN (SEANN), which evolves more flexible ANN structures to perform multi-step ahead forecasts. The second one, consists of an automatic Time lag feature selection EANN (TEANN) approach that evolves not only ANN parameters (e.g., input and hidden nodes, training parameters) but also which set of time lags are fed into the forecasting model. Several experiments were held, using a set of six time series, from different real-world domains. Also, two error metrics (i.e., Mean Squared Error and Symmetric Mean Absolute Per- centage Error) were analyzed. The two EANN approaches were compared against a base EANN (with no ANN structure or time lag optimization) and four other methods (Autoregressive Integrated Moving Average method, Random Forest, Echo State Network and Support Vector Machine). Overall, the proposed SEANN and TEANN methods obtained the best forecasting results. Moreover, they favor simpler neural network models, thus requiring less computational effort when compared with the base EANN.The research reported here has been supported by the Spanish Ministry of Science and Innovation under project TRA2010-21371-C03-03 and FCT - Fundacao para a Ciencia e Tecnologia within the Project Scope PEst- OE/EEI/UI0319/2014. The authors want to thank specially Martin Stepnicka and Lenka Vavrickova for all their help. The authors also want to thank Ramon Sagarna for introducing the subject of EDA