The Extraction of Trading Rules From Stock Market Data Using Rough Sets

We propose the rough set approach to the extraction of trading rules for discriminating between bullish and bearish patterns in the stock market. Rough set theory is quite valuable for extracting trading rules because it can be used to discover dependences in data while reducing the effect of superfluous factors in noisy data. In addition, it does not generate a signal to trade when the pattern of the market is uncertain because the selection of reducts and the extraction of rules are controlled by the strength of each reduct and rule. The experimental results are encouraging and show the usefulness of the rough set approach for stock market analysis with respect to profitability.     

基于GA与RST的分类规则挖掘算法

本文提出了在没有任何领域知识可供借鉴的情况下,利用遗传算法对信息系统的数量型属性进行离散化,利用RST进行分类规则挖掘,将GA与RST相结合进行分类规则挖掘的新算法。该算法不仅有效地解决了利用粗糙集理论进行分类规则挖掘时,数量型属性的离散化问题,而且可挖掘出通用的分类规则。     

Rough milling optimisation for parts with sculptured surfaces using genetic algorithms in a Stackelberg game

In rough milling of sculptured surface parts, decisions on process parameters concern feedrate, spindle speed, cutting speed, width of cut, raster pattern angle and number of machining slices of variable thickness. In this paper three rough milling objectives are considered: minimum machining time, maximum removed material and maximum uniformity of the remaining volume at the end of roughing. Owing to the complexity of the modelled problem and the large number of parameters, typical genetic algorithms cannot achieve global optima without defining case-dependent constraints. Therefore, to achieve generality, a hierarchical game similar to a Stackelberg game is implemented in which a typical Genetic Algorithm functions as the leader and micro-Genetic Algorithms as followers. In this game, one of the leader’s parameters is responsible for creating a follower’s population and for triggering the optimisation. After properly weighing the three objectives, the follower performs single-objective optimization in steps and feeds the leader back with the objective values as they appear prior to weighing. Micro-Genetic Algorithm (follower) chromosome consists of the distribution of machining slice thickness, while the typical Genetic Algorithm (leader) consists of the milling parameters. The methodology is tested on sculptured surface parts with different properties, and a representative case is presented here.     

一种不一致不完备信息系统的最优选择及规则约简方法研究

在分析不一致不完备信息系统规则提取的基础上,提出了先将不完备信息系统分为一致的和不一致的信息系统后再求其最优选择的方法。然后利用改进的分辨矩阵对所求得的不一致最优选择进行决策规则提取,并给出不确定决策规则的决策精度。最后,通过应用例子证明了本文提出方法的有效性。     

A genetic approach to the design of autonomous agents for futures trading

We propose a genetic algorithm-based method for designing an autonomous trader agent. The task of the proposed method is to find an optimal set of fuzzy if–then rules that best represents the behavior of a target trader agent. A highly profitable trader agent is used as the target in the proposed genetic algorithm. A trading history for the target agent is obtained from a series of futures trading. The antecedent part of fuzzy if–then rules considers time-series data of spot prices, while the consequent part indicates the order of trade (Buy, Sell, or No action) with its degree of certainty. The proposed method determines the antecedent part of fuzzy if–then rules. The consequent part of fuzzy if–then rules is automatically determined from the trading history of the target trader agent. The autonomous trader agent designed by the proposed genetic algorithm consists of a fixed number of fuzzy if–then rules. The decision of the autonomous trader agent is made by fuzzy inference from the time-series data of spot prices. This work was presented in part at the 11th International Symposium on Artificial Life and Robotics, Oita, Japan, January 23–25, 2006     

一种基于Rough集的兴趣缺省规则挖掘算法

Rough集理论提供了一种新的处理不精确,不完全与不相容知识的数学方法,从不一致决策表中快速,有效地挖掘出缺省规则是决策规则挖掘研究的一个热点,文中引入了决策规则的相似度概念,并提出了基于Rough集的兴趣缺省规则挖掘算法（IDRMA）,依据IDRMA算法对决策规则进行合理的分类,可获取用户的兴趣缺省规则。     

基于Rough集的规则学习研究

Rough Sets方法是一种处理不确定或模糊知识的重要工具,本文在对Rough Sets理论进行深入研究的基础上,提出了一种基于Rough Sets的自增量学习算法,该算法利用简化的差异矩阵和置信度,能较好地进行确定性规则和非确定性规则的学习。     

基于一般二元关系下的粗糙Vague集

本文研究了一般关系下Vague集合的近似问题,建立了一般关系下粗糙Vague近似的框架。在分析经典的粗集理论、模糊集理论、Vague集理论三者关系的基础上,提出了一般关系下粗糙Vague集的概念,并定义了粗糙Vague近似算子,讨论了粗糙Vague的性质。本文的结果对进一步开展粗糙集Vague集的研究具有一定的意义。     

基于Rough集距离函数的属性值区间化及其应用

Rough集作为一种处理模糊和不确定性问题的新颖方法已经在许多领域得到了广泛的应用。本文通过计算使决策表中的属性值区间化[3 ] ,删去冗余的决策规则 ,从而生成一种更具代表性的数据隐含式。利用上下距离函数和Rough决策因子代替统计方法或专家经验方法 ,在实际开发的系统中更具实用性。     

基于粗糙集的区间值属性决策表的有序规则获取

提出一种基于粗糙集的区间值属性决策表的有序规则获取方法。首先根据区间数之间基于可能度的序关系,将区间值属性决策表转化为二元决策表,然后利用粗糙集理论进行分析并推理出最优规则,最后再将二元决策表的规则转化为区间值属性决策表的有序规则。实验分析表明了该方法的有效性。     

不完备信息系统中知识获取算法

粗糙集理论是一种新的处理模糊和不确定知识的软计算工具.应用粗糙集理论,可以将隐藏在系统的知识能够以决策规则的形式表达出来.根据粗糙集上下近似的概念,决策规则能够分成确定性规则和可能性规则两种.本文将介绍从不完备信息系统中知识获取的算法,通过这些算法能够从不完备决策表中生成一种确定性的规则和两种可能性的规则,同时也介绍了不完备决策表中描述约简的算法.     

基于粗糙集的两阶段规则提取算法与有效性度量

在总结分析现有规则提取算法的基础上,提出了一种新的带覆盖度和置信度因子的两阶段规则提取算法,以解决噪音数据产生的噪音规则问题,并提出了一个衡量算法有效性的指标——支持度。实例表明,该算法能够有效地去除噪音规则,使提取出的规则更加精练实用。     

粗糙集理论的新进展及其在智能信息处理中的应用

主要总结了近年来粗糙集理论的研究和进展,介绍了广义粗糙集模型研究的一些主要方面和最新成果,从逼近算子和粗糙隶属函数的角度,讨论了广义粗糙集模型的各种类型,并着重分析了粗集理论在智能信息处理中的应用情况。     

Rough Set GA-based Hybrid Method for Robot Path Planning

In this paper, a hybrid method based on rough sets and genetic algorithms, is proposed to improve the speed of robot path planning. Decision rules are obtained using rough set theory. A series of available paths are produced by training obtained minimal decision rules. Path populations are optimised by using genetic algorithms until the best path is obtained. Experiment results show that this hybrid method is capable of improving robot path planning speed.     

A Rough Set GA-based Hybrid Method for Robot Path Planning

In this paper, a hybrid method based on rough sets and genetic algorithms, is proposed to improve the speed of robot path planning. Decision rules are obtained using rough set theory. A series of available paths are produced by training obtained minimal decision rules. Path populations axe optimised by using genetic algorithms until the best path is obtained. Experiment results show that this hybrid method is capable of improving robot path planning speed.     

Developing soft sensors using hybrid soft computing methodology: a neurofuzzy system based on rough set theory and genetic algorithms

This paper presents a hybrid soft computing modeling approach, a neurofuzzy system based on rough set theory and genetic algorithms (GA). To solve the curse of dimensionality problem of neurofuzzy system, rough set is used to obtain the reductive fuzzy rule set. Both the number of condition attributes and rules are reduced. Genetic algorithm is used to obtain the optimal discretization of continuous attributes. The fuzzy system is then represented via an equivalent artificial neural network (ANN). Because the initial parameter of the ANN is reasonable, the convergence of the ANN training is fast. After the rules are reduced, the structure size of the ANN becomes small, and the ANN is not fully weight-connected. The neurofuzzy approach based on RST and GA has been applied to practical application of building a soft sensor model for estimating the freezing point of the light diesel fuel in fluid catalytic cracking unit.     

交易备份系统的网络和安全设计

期货经纪公司的交易系统一旦发生故障，就可以采用此备份系统来保证该公司客户继续进行期货交易。为达到此目的，期货交易备份系统必段能够实时采集经纪公司的交易和结算数据，并且能够同时为多个经纪公司服务，运行多个不同的柜面系统，提供较为全面的安全防护。本文主要介绍了该备份系统的整体网络架构以及所采用的安全措施。     

广义信息系统及其决策规则

提出一类更广泛的信息系统,称其为广义信息系统．它包含了完备信息系统、不完备信息系统和多值信息系统．给出了广义信息系统集合近似的概念、方法和相关性质．讨论了广义信息决策系统的决策描述形式．这种决策描述形式易于转化为Skolem标准型,可以直接应用于人工智能的归结推理．     

Neuro-fuzzy approach versus rough-set inspired methodology for intelligent decision support

One of the two goals of this paper is to briefly present two different methodologies that can be used to the design of intelligent decision support systems, in particular, from the field of medicine. The first approach, combining artificial neural networks and fuzzy sets, yields a neuro-fuzzy classifier that can be trained with both purely numerical data as well as qualitative, linguistic, fuzzy data that describe the decision-making process. The second approach – resulting in a rough classifier – combines all positive aspects of rule induction systems with the flexibility of statistical techniques for classification. The second goal of this paper is to perform a broad comparative analysis of both proposed methodologies (and two others) applied to: (a) the problem of selecting surgical and non-surgical cases in the veterinary domain of equine colic, (b) the problem of diagnosing benign and malign types of breast cancer, and (c) the problem of corporate bankruptcy prediction (corporate ‘financial health'). Several aspects of comparison have been considered including the accuracy of the systems, diversity of the data processed, transparency and the form of decisions made.     

一种基于决策表的分类规则挖掘新算法

The mining of classification rules is an important field in Data Mining. Decision table of rough sets theory is an efficient tool for mining classification rules. The elementary concepts corresponding to decision table of Rough Sets Theory are introduced in this paper. A new algorithm for mining classification rules based on Decision Table is presented, along with a discernable function in reduction of attribute values, and a new principle for accuracy of rules. An example of its application to the car‘s classification problem is included, and the accuracy of rules discovered is analyzed. The potential fields for its application in data mining are also discussed.