Enhanced risk management by an emerging multi-agent architecture

Lin, Sin-Jin; Hsu, Ming-Fu

doi:10.1080/09540091.2014.908821

Cited by 6 publications

(4 citation statements)

References 48 publications

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“…According to our empirical results, the forecasting model with FS or FE can yield better performance than forecasting model with no FS or no FE technique. This finding is corresponded to prior works [19,30,[35][36]. The benefits of dimensionality reduction (FS or FE) include decreasing the computational complexity, saving the storage space, enhance the forecasting quality and interpreting complicated dependencies among attributes.…”

Section: Resultssupporting

confidence: 80%

Detection of Financial Information Manipulation by an Ensemble-Based Mechanism

Shih¹,

Lin²,

Hsu³

2014

NNW

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This tutorial is based on modification of the professor nomination lecture presented two years ago in front of the Scientific Council of the Czech Technical University in Prague [16].It is devoted to the techniques for the models developing suitable for processes forecasting in complex systems. Because of the high sensitivity of the processes to the initial conditions and, consequently, due to our limited possibilities to forecast the processes for the long-term horizon, the attention is focused on the techniques leading to practical applications of the short term prediction models. The aim of this tutorial paper is to bring attention to possible difficulties which designers of the predicting models and their users meet and which have to be solved during the prediction model developing, validation, testing, and applications. The presented overview is not complete, it only reflects the author's experience with developing of the prediction models for practical tasks solving in banking, meteorology, air pollution and energy sector. The paper is completed by an example of the global solar radiation prediction which forms an important input for the electrical energy production forecast from renewable sources. The global solar radiation forecasting is based on numerical weather prediction models. The time-lagged ensemble technique for uncertainty quantification is demonstrated on a simple example.

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Section: Resultssupporting

confidence: 80%

Detection of Financial Information Manipulation by an Ensemble-Based Mechanism

Shih¹,

Lin²,

Hsu³

2014

NNW

Self Cite

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“…This method is utilized in conjunction with other learning approaches having explanation capability that learn what HELM has learned. This study adopts the rough set theory (RST) [37] as the knowledge generator, because it has numerous advantages: (1) it yields a human-readable representation in an ''if (condition), then (decision)'' style [14,34]; (2) it handles vagueness and uncertainty in decision making [1,20,28,45,47,48,53]; (3) it is grounded only on the initial data and does not require additional information, unlike the grade of membership in the fuzzy set theory or probability in statistics [18,43]; and (4) the informative rules induced from RST are based on facts, because each decision rule is supported by a set of real-life examples [17]. The knowledge generated from HELM by RST can be viewed as a roadmap for decision makers to make reliable judgments.…”

Section: Hybrid Ensemble Learning Forecasting Mechanism (Helm)mentioning

confidence: 99%

An emerging hybrid mechanism for information disclosure forecasting

Hsu

Lin

2014

Int. J. Mach. Learn. & Cyber.

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Corporate governance mechanisms ensure that investors get a fair return on their investment. A wellestablished governance mechanism reduces the information asymmetry and agency cost between a firm's management and stakeholders, but decision makers find it difficult to assess the corporate governance status of publicly-listed firms before the annual official announcement the following year. This study proposes a hybrid ensemble learning forecasting mechanism (HELM), whose single-component candidates from the extreme learning machine (ELM) algorithm with dissimilar ensemble strategies (that is, data diversity, parameter diversity, kernel diversity, and preprocessing diversity) form one initial dataset. We implement locally linear embedding into the proposed mechanism to handle the dimensionality task and then utilize the weighted voting taken from the base components' crossvalidation performance on a training dataset as the integration mechanism. Experimental results show that the proposed HELM significantly outperforms the other classifiers, but its superior performance under many real-life application domains comes with a critical drawback: it is incapable of providing an explanation for the underlying reasoning mechanisms. Thus, this study advances the utilized rough set theory with its explanation capability to extract the inherent knowledge from the ensemble mechanism (HELM). The informative rules can be used as a guideline for decision makers to make a reliable judgment under turbulent financial markets.

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“…The importance of this problem comes from its presence in a high number of realworld problems, becoming one of the top challenges in data mining research [58]. We can find imbalanced distributions in areas like risk management [59], bioactivity of chemical substances [60], fraud detection [61], system failure detection [62] or medical applications [63,64], just mentioning some of them.…”

Section: Introduction To Classification With Imbalanced Datasetsmentioning

confidence: 99%

Evolutionary Fuzzy Systems: A Case Study in Imbalanced Classification

Fernández

Herrera

2016

Fuzzy Logic and Information Fusion

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The use of evolutionary algorithms for designing fuzzy systems provides them with learning and adaptation capabilities, resulting on what is known as Evolutionary Fuzzy Systems. These types of systems have been successfully applied in several areas of Data Mining, including standard classification, regression problems and frequent pattern mining. This is due to their ability to adapt their working procedure independently of the context we are addressing. Specifically, Evolutionary Fuzzy Systems have been lately applied to a new classification problem showing good and accurate results. We are referring to the problem of classification with imbalanced datasets, which is basically defined by an uneven distribution between the instances of the classes. In this work, we will first introduce some basic concepts on linguistic fuzzy rule based systems. Then, we will present a complete taxonomy for Evolutionary Fuzzy Systems. Then, we will review several significant proposals made in this research area that have been developed for addressing classification with imbalanced datasets. Finally, we will show a case study from which we will highlight the good behavior of Evolutionary Fuzzy Systems in this particular context.

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Enhanced risk management by an emerging multi-agent architecture

Cited by 6 publications

References 48 publications

Detection of Financial Information Manipulation by an Ensemble-Based Mechanism

Detection of Financial Information Manipulation by an Ensemble-Based Mechanism

An emerging hybrid mechanism for information disclosure forecasting

Evolutionary Fuzzy Systems: A Case Study in Imbalanced Classification

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