Forecasting PGR of the financial industry using a rough sets classifier based on attribute-granularity

In the financial industry, continually changing economic conditions and characteristics involving uncertainty and risk have made financial forecasts even more difficult, increasing the need for more reliable ways to forecast a bank’s operating performance. However, early related studies of performance analysis for using statistical methods usually become more complex when relationships in input/output data are nonlinear. Furthermore, strict data assumptions, such as linearity, normality, and independence, limit real-world applications often. Additionally, a drawback of traditional rough sets is that data must be discretized first for improving classification accuracy. To remedy the existing shortcomings above, the study proposes a hybrid procedure, which mixes professional knowledge, an attribute granularity, and a rough sets classifier, for automatically classifying profit growth rate (PGR) to solve real problems faced by investors. The proposed procedure is illustrated by examining a practical dataset for publicly traded financial holding stocks in Taiwan‘s stock markets. The experimental results reveal that the proposed procedure outperforms listing methods in terms of accuracy, and they provide useful insights in responsiveness to rapidly changing stock market conditions. Importantly, the output created by the rough sets LEM2 (Learning from Examples Module, version 2) algorithm is a set of comprehensible rules applied in a knowledge-based investment system for investors.     

A soft-computing based rough sets classifier for classifying IPO returns in the financial markets

In the financial markets, due to limitations of the noise caused continuously by changing market conditions and environments, and a subjective sentiment or other factors unrelated to expected returns on investment decision-making of investors, there is a growing consensus designing and employing a variety of soft computing systems to remedy the aforementioned existing problems objectively and intelligently. Previously, many researchers have long used statistical methods for handling the related problems of investment markets. However, these conventional methods become more complex when relationships in the input/output dataset are nonlinear. Nevertheless, statistical techniques always rely on the assumptions on linear separability, multivariate normality, and independence of the predictive variables; unfortunately, many of the common models of treating the financial markets problems violate these assumptions. Therefore, to reconcile the existing shortcomings, this study offers three hybrid models based on a rough sets classifier to extract decision rules and aid making investment decision for the market investors.The proposed hybrid models include three differently integrated models for solving IPO (Initial Public Offerings) returns problems of the financial markets: (1) Experiential Knowledge (EK) + Feature Selection Method (FSM) + Minimize Entropy Principle Approach (MEPA) + Rough Set Theory (RST) + Rule Filter (RF), (2) EK + Decision Trees (DT)-C4.5 + RST + RF, and (3) EK + FSM + RST + RF. The proposed hybrid models are illustrated by examining an IPO dataset for publicly traded firms. The experimental results indicate that the proposed hybrid models outperform the listing methods in accuracy, number of attributes, standard deviation, and number of rules. Furthermore, the proposed hybrid models generate comprehensible rules readily applied in knowledge-based systems for investors. Meaningfully, the study findings and implications are of value to both academicians and practitioners.     

基于粗糙变精度的食品安全决策树研究

食品安全决策是食品安全问题研究的一项重要内容。为了对食品安全状况进行分析,基于粗糙集变精度模型,提出了一种包含规则置信度的构造决策树新方法。这种新方法针对传统加权决策树生成算法进行了改进,新算法以加权平均变精度粗糙度作为属性选择标准构造决策树,用变精度近似精度来代替近似精度,可以在数据库中消除噪声冗余数据,并且能够忽略部分矛盾数据,保证决策树构建过程中能够兼容部分存在冲突的决策规则。该算法可以在生成决策树的过程中,简化其生成过程,提高其应用范围,并且有助于诠释其生成规则。验证结果表明该算法是有效可行的。     

Financial time-series analysis with rough sets

We investigate the use of the rough set model for financial time-series data analysis and forecasting. The rough set model is an emerging technique for dealing with vagueness and uncertainty in data. It has many advantages over other techniques, such as fuzzy sets and neural networks, including attribute reduction and variable partitioning of data. These characteristics can be very useful for improving the quality of results from data analysis. We demonstrate a rough set data analysis model for the discovery of decision rules from time series data for example, the New Zealand stock exchanges. Rules are generated through reducts and can be used for future prediction. A unique ranking system for the decision rules based both on strength of the rule and stability of the rule is used in this study. The ranking system gives the user confidence regarding their market decisions. Our experiment results indicate that the forecasting of future stock index values using rough sets obtains decision ruleswith high accuracy and coverage.     

An improved random forest-based rule extraction method for breast cancer diagnosis

Breast cancer has been becoming the main cause of death in women all around the world. An accurate and interpretable method is necessary for diagnosing patients with breast cancer for well-performed treatment. Nowadays, a great many of ensemble methods have been widely applied to breast cancer diagnosis, capable of achieving high accuracy, such as Random Forest. However, they are black-box methods which are unable to explain the reasons behind the diagnosis. To surmount this limitation, a rule extraction method named improved Random Forest (RF)-based rule extraction (IRFRE) method is developed to derive accurate and interpretable classification rules from a decision tree ensemble for breast cancer diagnosis. Firstly, numbers of decision tree models are constructed using Random Forest to generate abundant decision rules available. And then a rule extraction approach is devised to detach decision rules from the trained trees. Finally, an improved multi-objective evolutionary algorithm (MOEA) is employed to seek for an optimal rule predictor where the constituent rule set is the best trade-off between accuracy and interpretability. The developed method is evaluated on three breast cancer data sets, i.e., the Wisconsin Diagnostic Breast Cancer (WDBC) dataset, Wisconsin Original Breast Cancer (WOBC) dataset, and Surveillance, Epidemiology and End Results (SEER) breast cancer dataset. The experimental results demonstrate that the developed method can primely explain the black-box methods and outperform several popular single algorithms, ensemble learning methods, and rule extraction methods from the view of accuracy and interpretability. What is more, the proposed method can be popularized to other cancer diagnoses in practice, which provides an option to a more interpretable, more accurate cancer diagnosis process.     

基于拓展粗糙集的不完备表的规则挖掘及应用

实际应用中常存在缺省属性值的不完备信息系统,如何从不完备信息系统中挖掘有用规则是一个非常有价值的问题。粗糙集理论是一种有效的数据挖掘手段,但经典粗糙集缺乏对不完备信息系统的处理能力。在粗糙集拓展的基础上,设计出从不完备决策表中挖掘出有用规则的算法,并将其应用到银行贷款决策中不完备决策表的实例分析中。     

多粒度支持直觉模糊粗糙集的多属性决策方法

针对多属性决策中多个相互冲突的属性信息使决策者很难做出决策判断的问题,文中从支持直觉模糊集的角度研究该问题.首先,在支持直觉模糊集的基础上,结合多粒度粗糙集理论,构造乐观、悲观两种多粒度支持直觉模糊粗糙集模型,分析两种模型之间的相互关系,讨论相关性质.然后,利用t-模和t-余模定义拟合函数,提出多粒度支持直觉模糊粗糙集的多属性决策求解方法,同时定义得分函数和精确函数排序决策结果,提取相应的决策规则,设计算法.实例分析表明,文中方法使决策者在处理信息冲突的多属性决策问题时可根据实际需求选择最优决策方案     

Knowledge acquisition in vague objective information systems based on rough sets

Abstract: The growing volume of vague information poses interesting challenges and calls for new theories, techniques and tools for analysis of vague data sets. In this paper, we study how to extract knowledge from vague objective information systems (VOISs) based on rough sets theory. We first introduce the basic notion termed rough vague sets by combining rough sets theory and vague sets theory. By using the rough vague lower approximation distribution in the VOIS, the concept of attribute reduction is introduced. Then, we develop an algorithm based on a discernibility matrix to compute all the attribute reductions. Finally, a viable approach for extracting decision rules from the VOIS is proposed. An example is also presented to illustrate the application of the proposed theories and approaches in handling medical diagnosis problems.     

一种基于Rough集获取规则知识的增量式学习方法

将Rough集理论应用于规则归纳系统,提出了一种基于粗糙集获取规则知识库的增量式学习方法,能够有效处理决策表中不一致情形,采用启发式算法获取决策表的最简规则,当新对象加入时在原有规则集基础上进行规则知识库的增量式更新,避免了为更新规则而重新运行规获取算法。并用UCI中多个数据集从规则集的规则数目、数据浓缩率、预测能力等指标对该算法进行了测试。实验表明了该算法的有效性。     

基于相对决策嫡的决策树算法及其在入侵检测中的应用

为了弥补传统决策树算法的不足,提出一种基于相对决策熵的决策树算法DTRDE。首先,将Shannon提出的信息熵引入到粗糙集理论中,定义一个相对决策熵的概念,并利用相对决策熵来度量属性的重要性;其次,在算法DTRDE中,采用基于相对决策熵的属性重要性以及粗糙集中的属性依赖性来选择分离属性,并且利用粗糙集中的属性约简技术来删除冗余的属性,旨在降低算法的计算复杂性;最后,将该算法应用于网络入侵检测。在KDD Cup99数据集上的实验表明,DTRDE算法比传统的基于信息熵的算法具有更高的检测率,而其计算开销则与传统方法接近。     

A Comparison of the Rough Sets and Recursive Partitioning Induction Approaches: An Application to Commercial Loans

Credit scoring is the term used to describe methods utilized for classifying applicants for credit into classes of risk. This paper evaluates two induction approaches, rough sets and decision trees, as techniques for classifying credit (business) applicants. Inductive learning methods, like rough sets and decision trees, have a better knowledge representational structure than neural networks or statistical procedures because they can be used to derive production rules. If decision trees have already been used for credit granting, the rough sets approach is rarely utilized in this domain. In this paper, we use production rules obtained on a sample of 1102 business loans in order to compare the classification abilities of the two techniques. We show that decision trees obtain better results with 87.5% of good classifications with a pruned tree, against 76.7% for rough sets. However, decision trees make more type–II errors than rough sets, but fewer type–I errors.     

An enhanced classification method comprising a genetic algorithm, rough set theory and a modified PBMF-index function

This study proposes a method, designated as the GRP-index method, for the classification of continuous value datasets in which the instances do not provide any class information and may be imprecise and uncertain. The proposed method discretizes the values of the individual attributes within the dataset and achieves both the optimal number of clusters and the optimal classification accuracy. The proposed method consists of a genetic algorithm (GA) and an FRP-index method. In the FRP-index method, the conditional and decision attribute values of the instances in the dataset are fuzzified and discretized using the Fuzzy C-means (FCM) method in accordance with the cluster vectors given by the GA specifying the number of clusters per attribute. Rough set (RS) theory is then applied to determine the lower and upper approximate sets associated with each cluster of the decision attribute. The accuracy of approximation of each cluster of the decision attribute is then computed as the cardinality ratio of the lower approximate sets to the upper approximate sets. Finally, the centroids of the lower approximate sets associated with each cluster of the decision attribute are determined by computing the mean conditional and decision attribute values of all the instances within the corresponding sets. The cluster centroids and accuracy of approximation are then processed by a modified form of the PBMF-index function, designated as the RP-index function, in order to determine the optimality of the discretization/classification results. In the event that the termination criteria are not satisfied, the GA modifies the initial population of cluster vectors and the FCM, RS and RP-index function procedures are repeated. The entire process is repeated iteratively until the termination criteria are satisfied. The maximum value of the RP cluster validity index is then identified, and the corresponding cluster vector is taken as the optimal classification result. The validity of the proposed approach is confirmed by cross validation, and by comparing the classification results obtained for a typical stock market dataset with those obtained by non-supervised and pseudo-supervised classification methods. The results show that the proposed GRP-index method not only has a better discretization performance than the considered methods, but also achieves a better accuracy of approximation, and therefore provides a more reliable basis for the extraction of decision-making rules.     

An illustration of variable precision rough sets model: an analysis of the findings of the UK Monopolies and Mergers Commission

This paper introduces a new technique in the investigation of limited-dependent variable models. This paper illustrates that variable precision rough set theory (VPRS), allied with the use of a modern method of classification, or discretisation of data, can out-perform the more standard approaches that are employed in economics, such as a probit model. These approaches and certain inductive decision tree methods are compared (through a Monte Carlo simulation approach) in the analysis of the decisions reached by the UK Monopolies and Mergers Committee. We show that, particularly in small samples, the VPRS model can improve on more traditional models, both in-sample, and particularly in out-of-sample prediction. A similar improvement in out-of-sample prediction over the decision tree methods is also shown.Scope and purposeThe Monopolies and Mergers Commission (MMC) in the UK evaluates whether a given firm, or set of firms is behaving in a manner that is considered to be against the public interest, that is anti-competitive. The interpretation and prediction of the decisions made by the MMC is of importance to firm's possible future investment plans. Through the construction of decision rules using the variable precision rough sets (VPRS) model this interoperation and prediction is able to be undertaken. The importance of the concomitant variables in the decisions made is shown through a ‘leave n out’ Monte Carlo simulation approach. At the technical level this study illustrates one of the first applications of VPRS in an economic environment.     

基于二元决策系统的粗集知识获取方法研究

提出一种新的粗集知识获取方法,首先将事例集表示成二元决策系统,然后奖其分解成一系列单一二元决策子系统。利用粗集理论对每一子系统进行分析,推理出最优规则。在对决策系统进行条件属性和规则简化时,提出了概率最佳简约准则和概率最小规则准则,按照这两种最优准则可以获得概率意义上数目最小规则集。通过实例分析,具体说明了该方法的实现步骤,结果表明该方法具有明显的优越性。     

一种基于粗糙集理论的最简决策规则挖掘算法

研究粗糙集理论中可辨识矩阵，扩展了类别特征矩阵，提出一种基于粗糙集理论的最筒决策规则算法．该算法根据决策属性将原始决策表分成若干个等价子决策表．借助核属性和属性频率函数对各类别特征矩阵挖掘出最简决策规则．与可辨识矩阵相比，采用类别特征矩阵可有效减少存储空间和时间复杂度。增强规则的泛化能力．实验结果表明，采用所提出的算法获得的规则更为简洁和高效．     

An intelligent hybrid trading system for discovering trading rules for the futures market using rough sets and genetic algorithms

Discovering intelligent technical trading rules from nonlinear and complex stock market data, and then developing decision support trading systems, is an important challenge. The objective of this study is to develop an intelligent hybrid trading system for discovering technical trading rules using rough set analysis and a genetic algorithm (GA). In order to obtain better trading decisions, a novel rule discovery mechanism using a GA approach is proposed for solving optimization problems (i.e., data discretization and reducts) of rough set analysis when discovering technical trading rules for the futures market. Experiments are designed to test the proposed model against comparable approaches (i.e., random, correlation, and GA approaches). In addition, these comprehensive experiments cover most of the current trading system topics, including the use of a sliding window method (with or without validation dataset), the number of trading rules, and the size of training period. To evaluate an intelligent hybrid trading system, experiments were carried out on the historical data of the Korea Composite Stock Price Index 200 (KOSPI 200) futures market. In particular, trading performance is analyzed according to the number of sets of decision rules and the size of the training period for discovering trading rules for the testing period. The results show that the proposed model significantly outperforms the benchmark model in terms of the average return and as a risk-adjusted measure.     

Rule induction for hierarchical attributes using a rough set for the selection of a green fleet

Rough set theory (RST) has been the subject of much study and numerous applications in many areas. However, most previous studies on rough sets have focused on finding rules where the decision attribute has a flat, rather than hierarchical structure. In practical applications, attributes are often organized hierarchically to represent general/specific meanings. This paper (1) determines the optimal decision attribute in a hierarchical level-search procedure, level by level, (2) merges the two stages, generating reducts and inducting decision rules, into a one-shot solution that reduces the need for memory space and the computational complexity and (3) uses a revised strength index to identify meaningful reducts and to improve their accuracy. The selection of a green fleet is used to validate the superiority of the proposed approach and its potential benefits to a decision-making process for transportation industry.     

Dominance relation and rules in an incomplete ordered information system

Rough sets theory has proved to be a useful mathematical tool for classification and prediction. However, as many real‐world problems deal with ordering objects instead of classifying objects, one of the extensions of the classical rough sets approach is the dominance‐based rough sets approach, which is mainly based on substitution of the indiscernibility relation by a dominance relation. In this article, we present a dominance‐based rough sets approach to reasoning in incomplete ordered information systems. The approach shows how to find decision rules directly from an incomplete ordered decision table. We propose a reduction of knowledge that eliminates only that information that is not essential from the point of view of the ordering of objects or decision rules. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 13–27, 2005.     

MGRS: A multi-granulation rough set

The original rough set model was developed by Pawlak, which is mainly concerned with the approximation of sets described by a single binary relation on the universe. In the view of granular computing, the classical rough set theory is established through a single granulation. This paper extends Pawlak’s rough set model to a multi-granulation rough set model (MGRS), where the set approximations are defined by using multi equivalence relations on the universe. A number of important properties of MGRS are obtained. It is shown that some of the properties of Pawlak’s rough set theory are special instances of those of MGRS.Moreover, several important measures, such as accuracy measureα, quality of approximationγ and precision of approximationπ, are presented, which are re-interpreted in terms of a classic measure based on sets, the Marczewski-Steinhaus metric and the inclusion degree measure. A concept of approximation reduct is introduced to describe the smallest attribute subset that preserves the lower approximation and upper approximation of all decision classes in MGRS as well. Finally, we discuss how to extract decision rules using MGRS. Unlike the decision rules (“AND” rules) from Pawlak’s rough set model, the form of decision rules in MGRS is “OR”. Several pivotal algorithms are also designed, which are helpful for applying this theory to practical issues. The multi-granulation rough set model provides an effective approach for problem solving in the context of multi granulations.     

Application of rough set classifiers for determining hemodialysis adequacy in ESRD patients

The incidence and the prevalence of end-stage renal disease (ESRD) in Taiwan are the highest in the world. Therefore, hemodialysis (HD) therapy is a major concern and an important challenge due to the shortage of donated organs for transplantation. Previous researchers developed various forecasting models based on statistical methods and artificial intelligence techniques to address the real-world problems of HD therapy that are faced by ESRD patients and their doctors in the healthcare services. Because the performance of these forecasting models is highly dependent on the context and the data used, it would be valuable to develop more suitable methods for applications in this field. This study presents an integrated procedure that is based on rough set classifiers and aims to provide an alternate method for predicting the urea reduction ratio for assessing HD adequacy for ESRD patients and their doctors. The proposed procedure is illustrated in practice by examining a dataset from a specific medical center in Taiwan. The experimental results reveal that the proposed procedure has better accuracy with a low standard deviation than the listed methods. The output created by the rough set LEM2 algorithm is a comprehensible decision rule set that can be applied in knowledge-based healthcare services as desired. The analytical results provide useful information for both academics and practitioners.