Taiwan stock market exists a special feature that over 80% of participants are natural persons while only 20% are legal persons. Compared to the latter, natural persons own less expertise in stock trading. Thus the effectiveness of the local stock market is an interesting subject for research. In this paper, we will try to find out an answer through the using of technical analysis on the past two years trading data to see if it can gain benefit in investment.Most of the similar research in past exist some problems, which either use only single or a pair of technical indices for prediction, predict only a specific stock, or filter out unwanted training and testing data in preprocessing, etc. Thus their results may not really reflect the effectiveness of the market. In this paper, we will adopt a different way of experiment design to conduct the test.Past research has shown that a fuzzy decision tree outperforms a normal crisp decision tree in data classification when there are numerical attributes in the target domain to be classified (Y.M. Jeng, 1993). Since most of the technical indices are expressed in terms of numerical values, we therefore choose it as the tool to generate rules from the eight largest stocks out of the local stock market that have the largest capitals and highest turnover rate. The trees are evaluated with more objective criteria and used to predict the up or down of the stock prices in the next day. The experimental results show that the created fuzzy trees have a better predictive accuracy than a random walk, and the investment rewards based on the trees are much better than the buy-and- hold policy.

In the last one hundred years, the mankind has invented a lot of convenient tools for pursuing beautiful and comfortable living environment. Computer is one of the most important inventions, and its operation ability is incomparable with the mankind. Because computer can deal with a large amount of data fast and accurately, people use this advantage to imitate human thinking. Artificial intelligence is developed extensively. Methods, such as all kinds of neural networks, data mining, fuzzy logic, etc., apply to each side fields (ex: fingerprint distinguishing, image compressing, antennal designing, etc.). We will probe into to prediction technology according to the decision tree and fuzzy clustering. The fuzzy decision tree proposed the classification method by using fuzzy clustering method, and then construct out the decision tree to predict for data. However, in the distance function, the impact of the target space was proportional inversely. This situation could make problems in some dataset. Besides, the output model of each leaf node represented by a constant restricts the representation capability about the data distribution in the node. We propose a more reasonable definition of the distance function by considering both input and target differences with weighting factor. We also extend the output model of each leaf node to a local linear model and estimate the model parameters with a recursive SVD-based least squares estimator. Experimental results have shown that our improved version produces higher recognition rates and smaller mean square errors for classification and regression problems, respectively.

Unscheduled aircraft maintenance causes a lot problems and costs for aircraft operators. This is due to the fact that aircraft cause significant costs if flights have to be delayed or canceled and because spares are not always available at any place and sometimes have to be shipped across the world. Reducing the number of unscheduled maintenance is thus a great costs factor for aircraft operators. This thesis describes three methods for aircraft health monitoring and prediction; one method for system monitoring, one method for forecasting of time series and one method that combines the two other methods for one complete monitoring and prediction process. Together the three methods allow the forecasting of possible failures. The two base methods use decision trees for decision making in the processes and genetic optimization to improve the performance of the decision trees and to reduce the need for human interaction. Decision trees have the advantage that the generated code can be fast and easily processed, they can be altered by human experts without much work and they are readable by humans. The human readability and modification of the results is especially important to include special knowledge and to remove errors, which the automated code generation produced.

Submitted by Ronildo Prado (ronisp@ufscar.br) on 2017-08-08T20:30:11Z No. of bitstreams: 1 DissMVRL.pdf: 2179441 bytes, checksum: 3c4089c4b24a3d98521f8561c6f2c515 (MD5) / Approved for entry into archive by Ronildo Prado (ronisp@ufscar.br) on 2017-08-08T20:30:33Z (GMT) No. of bitstreams: 1 DissMVRL.pdf: 2179441 bytes, checksum: 3c4089c4b24a3d98521f8561c6f2c515 (MD5) / Approved for entry into archive by Ronildo Prado (ronisp@ufscar.br) on 2017-08-08T20:30:39Z (GMT) No. of bitstreams: 1 DissMVRL.pdf: 2179441 bytes, checksum: 3c4089c4b24a3d98521f8561c6f2c515 (MD5) / Made available in DSpace on 2017-08-08T20:31:24Z (GMT). No. of bitstreams: 1 DissMVRL.pdf: 2179441 bytes, checksum: 3c4089c4b24a3d98521f8561c6f2c515 (MD5) Previous issue date: 2016-03-03 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Inductive Decision Trees (DT) are mechanisms based on the symbolic paradigm of machine learning which main characteristics are easy interpretability and low computational cost. Though they are widely used, the DTs can represent problems with just discrete or continuous variables. However, for some problems, the variables are not well represented in this way. In order to improve DTs, the Fuzzy Decision Trees (FDT) were developed, adding the ability to deal with fuzzy variables to the Inductive Decision Trees, making them capable to deal with imprecise knowledge. In this text, it is presented a new algorithm for fuzzy decision trees induction. Its fuzification method is applied during the induction and it is inspired by the C4.5’s partitioning method for continuous attributes. The proposed algorithm was tested with 20 datasets from UCI repository (LICHMAN, 2013). It was compared with other three algorithms that implement different solutions to classification problem: C4.5, which induces an Inductive Decision Tree, FURIA, that induces a Rule-based Fuzzy System and FuzzyDT, which induces a Fuzzy Decision Tree where the fuzification is done before tree’s induction is performed. The results are presented in Chapter 4. / As Árvores de Decisão Indutivas (AD) são um mecanismo baseado no paradigma simbólico do Aprendizado de Máquina que tem como principais características a fácil interpretabilidade e baixo custo computacional. Ainda que sejam amplamente utilizadas, as ADs são limitadas à representação de problemas cujas variáveis são do tipo discreto ou contínuo. No entanto, para alguns tipos de problemas, pode haver variáveis que não são bem representadas por estes formatos. Diante deste contexto, foram criadas as Árvores de Decisão Fuzzy (ADF), que adicionam à interpretabilidade das Árvores de Decisão Indutivas, a capacidade de lidar com variáveis fuzzy, as quais representam adequadamente conhecimentos imprecisos. Neste texto, apresentamos o trabalho desenvolvido durante o mestrado, que tem como principal resultado um novo algoritmo para indução de Árvores de Decisão Fuzzy, cujo método de fuzificação dos atributos contínuos é realizado durante a indução da árvore e foi inspirado no método de particionamento de atributos contínuos adotado pelo C4.5. Para validação do algoritmo, foram realizados testes com 20 conjuntos de dados do repositório UCI (LICHMAN, 2013) e o algoritmo foi comparado com outros três algoritmos que abordam o problema de classificação por meio de técnicas diferentes: o C4.5 que induz uma Árvore de Decisão Indutiva, o FURIA, que induz um Sistema Fuzzy Baseado em Regras, porém não segue a estrutura de árvore e o FuzzyDT que induz uma Árvore de Decisão fuzzy realizando a fuzificação dos atributos contínuos antes da indução da árvore. Os resultados dos experimentos realizados são apresentados e discutidos no Capítulo 4 deste texto.

Reducing unscheduled maintenance is important for aircraft operators. There are significant costs if flights must be delayed or cancelled, for example, if spares are not available and have to be shipped across the world. This thesis describes three methods of aircraft health condition monitoring and prediction; one for system monitoring, one for forecasting and one combining the two other methods for a complete monitoring and prediction process. Together, the three methods allow organizations to forecast possible failures. The first two use decision trees for decision-making and genetic optimization to improve the performance of the decision trees and to reduce the need for human interaction. Decision trees have several advantages: the generated code is quickly and easily processed, it can be altered by human experts without much work, it is readable by humans, and it requires few resources for learning and evaluation. The readability and the ability to modify the results are especially important; special knowledge can be gained and errors produced by the automated code generation can be removed. A large number of data sets is needed for meaningful predictions. This thesis uses two data sources: first, data from existing aircraft sensors, and second, sound and vibration data from additionally installed sensors. It draws on methods from the field of big data and machine learning to analyse and prepare the data sets for the prediction process.