Agent based mobile negotiation for personalized pricing of last minute theatre tickets

This is the post-print version of the final paper published in Expert Systems with Applications. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2012 Elsevier B.V.This paper proposes an agent based mobile negotiation framework for personalized pricing of last minutes theatre tickets whose values are dependent on the time remaining to the performance and the locations of potential customers. In particular, case based reasoning and fuzzy cognitive map techniques are adopted in the negotiation framework to identify the best initial offer zone and adopt multi criteria decision in the scoring function to evaluate offers. The proposed framework is tested via a computer simulation in which personalized pricing policy shows higher market performance than other policies therefore the validity of the proposed negotiation framework.The Ministry of Education, Science and Technology (Korea

Prediction of Banks Financial Distress

In this research we conduct a comprehensive review on the existing literature of prediction techniques that have been used to assist on prediction of the bank distress. We categorized the review results on the groups depending on the prediction techniques method, our categorization started by firstly using time factors of the founded literature, so we mark the literature founded in the period (1990-2010) as history of prediction techniques, and after this period until 2013 as recent prediction techniques and then presented the strengths and weaknesses of both. We came out by the fact that there was no specific type fit with all bank distress issue although we found that intelligent hybrid techniques considered the most candidates methods in term of accuracy and reputatio

Predicting Bankruptcy After The Sarbanes-Oxley Act Using The Most Current Data Mining Approaches

Our study proposes several current data mining methods to predict bankruptcy after the Sarbanes-Oxley Act (2002) using 2007-2008 U.S. data.  The Sarbanes-Oxley Act (SOX) of 2002 was introduced to improve the quality of financial reporting and minimize corporate fraud in the U.S.  Because of this SOX implementation, a company’s financial statements are assumed to provide higher quality financial information for investors and other stakeholders. The results of our data mining approaches in our bankruptcy prediction study show that Bayesian Net method performs the best (85% overall prediction rate with 94% in AUC), followed by J48 (85% with 82% AUC), Decision Table (83.52%), and Decision Tree (82%) methods using financial and other data from the 10-K report and Compustat.  These results are better than previous bankruptcy prediction studies before the SOX implementation using most current data mining approaches

Predicting Corporate Bankruptcy: Lessons from the Past

The need for corporate bankruptcy prediction models arises in 1960 after the increase in incidence of some major bankruptcies. Over the years, the episodes of financial turmoil increase in number and so does these bankruptcy prediction models. Existing reviews of bankruptcy models are either narrowly focused or outdated. Current study aims to provide an overview of the existing models for predicting bankruptcy and review the significance of these models. Furthermore, it highlights the problems and issues in the existing models which hinders the accuracy in predicting bankruptcy

Bankruptcy Prediction: A Comparison of Some Statistical and Machine Learning Techniques

We are interested in forecasting bankruptcies in a probabilistic way. Specifically, we compare the classification performance of several statistical and machine-learning techniques, namely discriminant analysis (Altman's Z-score), logistic regression, least-squares support vector machines and different instances of Gaussian processes (GP's) -that is GP's classifiers, Bayesian Fisher discriminant and Warped GP's. Our contribution to the field of computational finance is to introduce GP's as a potentially competitive probabilistic framework for bankruptcy prediction. Data from the repository of information of the US Federal Deposit Insurance Corporation is used to test the predictions.Bankruptcy prediction, Artificial intelligence, Supervised learning, Gaussian processes, Z-score.

A Type-2 Fuzzy Logic Based System for Decision Support to Minimize Financial Default in the Sudanese Banking Sector

The recent global financial-economic crisis has led to the collapse of several companies from all over the world. This has created the need for powerful frameworks which can predict and reduce the potential risks in financial applications. Such frameworks help organizations to enhance their services quality and productivity as well as reducing the financial risk. The widely used techniques to build predictive models in the financial sector are based on statistical regression, which is deployed in many financial applications such as risk forecasting, customers’ loan default and fraud detection. However, in the last few years, the use of Artificial Intelligence (AI) techniques has increased in many financial institutions because they can provide powerful predictive models. However, the vast majority of the existing AI techniques employ black box models like Support Vector Machine (SVMs) and Neural Network (NNs) which are not able to give clear and transparent reasoning to explain the extracted decision. However, nowadays transparent reasoning models are highly needed for financial applications. This paper presents a type-2 fuzzy logic system for predicting default in financial systems. the researchers used a real dataset collected from the banking sector in Sudan. The proposed system resulted in transparent outputs which could be easily understood, analyzed and augmented by the human stakeholders. Besides, the proposed system resulted in an average recall of 83.5%, which outperformed its type-1 counterpart by 20.66%

Hybrid forecasting system based on case-based reasoning and analytic hierarchy process for cost estimation

Cost estimating of highway projects with high accuracy at the early stage of project development is crucial for planning and feasibility studies. Various research have been attempted to develop cost prediction models in the early stage of a construction life cycle. This study uses the hybrid estimating tool to provide an effective cost data management for highway projects and accordingly develops a realistic cost estimating system. This study focused on the development of a more accurate estimate technique for highway projects in South Korea at the early stage using hybrid analytic hierarchy process (AHP) and case-based reasoning (CBR). Real case studies are used to demonstrate and validate the benefits of the proposed approach. It is expected that the developed CBR system is to provide decision-makers with accurate cost information to asses and compare multiple alternatives for obtaining the optimal solution and controlling cost

Multiple proportion case-basing driven CBRE and its application in the evaluation of possible failure of firms

Case-based reasoning (CBR) is a unique tool for the evaluation of possible failure of firms (EOPFOF) for its eases of interpretation and implementation. Ensemble computing, a variation of group decision in society, provides a potential means of improving predictive performance of CBR-based EOPFOF. This research aims to integrate bagging and proportion case-basing with CBR to generate a method of proportion bagging CBR for EOPFOF. Diverse multiple case bases are first produced by multiple case-basing, in which a volume parameter is introduced to control the size of each case base. Then, the classic case retrieval algorithm is implemented to generate diverse member CBR predictors. Majority voting, the most frequently used mechanism in ensemble computing, is finally used to aggregate outputs of member CBR predictors in order to produce final prediction of the CBR ensemble. In an empirical experiment, we statistically validated the results of the CBR ensemble from multiple case bases by comparing them with those of multivariate discriminant analysis, logistic regression, classic CBR, the best member CBR predictor and bagging CBR ensemble. The results from Chinese EOPFOF prior to 3 years indicate that the new CBR ensemble, which significantly improved CBRs predictive ability, outperformed all the comparative methods