The Experts below are selected from a list of 11166 Experts worldwide ranked by ideXlab platform
Jerzy W Grzymalabusse - One of the best experts on this subject based on the ideXlab platform.
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an analysis of probabilistic approximations for Rule Induction from incomplete data sets
Rough Sets and Knowledge Technology, 2014Co-Authors: Patrick G Clark, Jerzy W Grzymalabusse, Zdzislaw S HippeAbstract:The main objective of our research was to test whether the probabilistic approximations should be used in Rule Induction from incomplete data. For our research we designed experiments using six standard data sets. Four of the data sets were incomplete to begin with and two of the data sets had missing attribute values that were randomly inserted. In the six data sets, we used two interpretations of missing attribute values: lost values and “do not care” conditions. In addition we used three definitions of approximations: singleton, subset and concept. Among 36 combinations of a data set, type of missing attribute values and type of approximation, for five combinations the error rate (the result of ten-fold cross validation) was smaller than for ordinary (lower and upper) approximations; for other four combinations, the error rate was larger than for ordinary approximations. For the remaining 27 combinations, the difference between these error rates was not statistically significant.
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how good are probabilistic approximations for Rule Induction from data with missing attribute values
International Conference on Rough Sets and Current Trends in Computing, 2012Co-Authors: Patrick G Clark, Jerzy W Grzymalabusse, Zdzislaw S HippeAbstract:The main objective of our research was to test whether the probabilistic approximations should be used in Rule Induction from incomplete data. Probabilistic approximations, well known for many years, are used in variable precision rough set models and similar approaches to uncertainty.
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experiments on Rule Induction from incomplete data using three probabilistic approximations
Granular Computing, 2012Co-Authors: Patrick G Clark, Jerzy W GrzymalabusseAbstract:We present results of experiments on Rule Induction using three probabilistic approximations: lower, middle, and upper. Our results were conducted on four typical series of incomplete data sets with 5% increments of missing attribute values. Two interpretations of missing attribute values were used: lost and “do not care” conditions. We conclude that the best approach (choice of the interpretation of missing attribute values and selection of the best type of approximation) depends on a data set. Probabilistic approximations are constructed from characteristic sets. The number of distinct probabilities associated with characteristic sets is much larger for data sets with “do not care” conditions than with data sets with lost values. Therefore, for data sets with “do not care” conditions the number of probabilistic approximations is also larger.
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an empirical comparison of Rule Induction using feature selection with the lem2 algorithm
International Conference Information Processing, 2012Co-Authors: Jerzy W GrzymalabusseAbstract:The main objective of this paper is to compare a strategy to Rule Induction based on feature selection with another strategy, not using feature selection, exemplified by the LEM2 algorithm. It is shown that LEM2 significantly outperforms the strategy or Rule Induction based on feature selection in terms of an error rate (5% significance level, two-tailed test). At the same time, the LEM2 algorithm induces smaller Rule sets with the smaller total number of conditions as well.
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increasing data set incompleteness may improve Rule set quality
International Conference on Software and Data Technologies, 2008Co-Authors: Jerzy W Grzymalabusse, Witold J GrzymalabusseAbstract:This paper presents a new methodology to improve the quality of Rule sets. We performed a series of data mining experiments on completely specified data sets. In these experiments we removed some specified attribute values, or, in different words, replaced such specified values by symbols of missing attribute values, and used these data for Rule Induction while original, complete data sets were used for testing. In our experiments we used the MLEM2 Rule Induction algorithm of the LERS data mining system, based on rough sets. Our approach to missing attribute values was based on rough set theory as well. Results of our experiments show that for some data sets and some interpretation of missing attribute values, the error rate was smaller than for the original, complete data sets. Thus, Rule sets induced from some data sets may be improved by increasing incompleteness of data sets. It appears that by removing some attribute values, the Rule Induction system, forced to induce Rules from remaining information, may induce better Rule sets.
Chingchin Chern - One of the best experts on this subject based on the ideXlab platform.
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an agent model for incremental rough set based Rule Induction a big data analysis in sales promotion
Hawaii International Conference on System Sciences, 2013Co-Authors: Yuneng Fan, Chingchin ChernAbstract:Rough set-based Rule Induction is able to generate decision Rules from a database and has mechanisms to handle noise and uncertainty in data. This technique facilitates managerial decision-making and strategy formulation. However, the process for RS-based Rule Induction is complex and computationally intensive. Moreover, operational databases that are used to run the day-to-day operations, thus large volumes of data are continually updated within a short period of time. The infrastructure required to analyze such large amounts of data must be able to handle extreme data volumes, to allow fast response times, and to automate decisions based on analytical models. This study proposes an Incremental Rough Set-based Rule Induction Agent (IRSRIA). Rule Induction is based on creating agents for the main modeling processes. In addition, an incremental architecture is designed, to address large-scale dynamic database problems. A case study of a Home shopping company is used to show the validity and efficiency of this method. The results of experiments show that the IRSRIA can considerably reduce the computation time for inducing decision Rules, while maintaining the same quality of Rules.
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an agent model for incremental rough set based Rule Induction in customer relationship management
Hybrid Artificial Intelligence Systems, 2012Co-Authors: Yuneng Fan, Chingchin ChernAbstract:Compared to other methods, rough set (RS) has the advantage of combining both qualitative and quantitative information in decision analysis, which is extremely important for customer relationship management (CRM). In this paper, we introduce an application of a multi-agent embedded incremental rough set-based Rule Induction to CRM, namely Incremental Rough Set-based Rule Induction Agent (IRSRIA). The Rule Induction is based on creating agents within the main modeling processes. This method is suitable for qualitative information and also takes into account user preferences. Furthermore, we designed an incremental architecture for addressing dynamic database problems of rough set-based Rule Induction, making it unnecessary to re-compute the whole dataset when the database is updated. As a result, huge degrees of computation time and memory space are saved when executing IRSRIA. Finally, we apply our method to a case study of a cell phone purchase. The results show the practical viability and efficiency of this method, and thus this paper forms the basis for solving many other similar problems that occur in the service industry.
Simon Fong - One of the best experts on this subject based on the ideXlab platform.
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composite monte carlo decision making under high uncertainty of novel coronavirus epidemic using hybridized deep learning and fuzzy Rule Induction
Applied Soft Computing, 2020Co-Authors: Simon Fong, Nilanjan Dey, Ruben Gonzalez Crespo, Enrique HerreraviedmaAbstract:In the advent of the novel coronavirus epidemic since December 2019, governments and authorities have been struggling to make critical decisions under high uncertainty at their best efforts. In computer science, this represents a typical problem of machine learning over incomplete or limited data in early epidemic Composite Monte-Carlo (CMC) simulation is a forecasting method which extrapolates available data which are broken down from multiple correlated/casual micro-data sources into many possible future outcomes by drawing random samples from some probability distributions. For instance, the overall trend and propagation of the infested cases in China are influenced by the temporal-spatial data of the nearby cities around the Wuhan city (where the virus is originated from), in terms of the population density, travel mobility, medical resources such as hospital beds and the timeliness of quarantine control in each city etc. Hence a CMC is reliable only up to the closeness of the underlying statistical distribution of a CMC, that is supposed to represent the behaviour of the future events, and the correctness of the composite data relationships. In this paper, a case study of using CMC that is enhanced by deep learning network and fuzzy Rule Induction for gaining better stochastic insights about the epidemic development is experimented. Instead of applying simplistic and uniform assumptions for a MC which is a common practice, a deep learning-based CMC is used in conjunction of fuzzy Rule Induction techniques. As a result, decision makers are benefited from a better fitted MC outputs complemented by min-max Rules that foretell about the extreme ranges of future possibilities with respect to the epidemic.
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composite monte carlo decision making under high uncertainty of novel coronavirus epidemic using hybridized deep learning and fuzzy Rule Induction
arXiv: Artificial Intelligence, 2020Co-Authors: Simon Fong, Nilanjan Dey, Ruben Gonzalez Crespo, Enrique HerreraviedmaAbstract:In the advent of the novel coronavirus epidemic since December 2019, governments and authorities have been struggling to make critical decisions under high uncertainty at their best efforts. Composite Monte-Carlo (CMC) simulation is a forecasting method which extrapolates available data which are broken down from multiple correlated/casual micro-data sources into many possible future outcomes by drawing random samples from some probability distributions. For instance, the overall trend and propagation of the infested cases in China are influenced by the temporal-spatial data of the nearby cities around the Wuhan city (where the virus is originated from), in terms of the population density, travel mobility, medical resources such as hospital beds and the timeliness of quarantine control in each city etc. Hence a CMC is reliable only up to the closeness of the underlying statistical distribution of a CMC, that is supposed to represent the behaviour of the future events, and the correctness of the composite data relationships. In this paper, a case study of using CMC that is enhanced by deep learning network and fuzzy Rule Induction for gaining better stochastic insights about the epidemic development is experimented. Instead of applying simplistic and uniform assumptions for a MC which is a common practice, a deep learning-based CMC is used in conjunction of fuzzy Rule Induction techniques. As a result, decision makers are benefited from a better fitted MC outputs complemented by min-max Rules that foretell about the extreme ranges of future possibilities with respect to the epidemic.
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Measuring Similarity by Prediction Class between Biomedical Datasets via Fuzzy Unordered Rule Induction
2016Co-Authors: Simon Fong, Osama Mohammed, Jinan Fiaidhi, Sabah Mohammed, Chee Keong KwohAbstract:The need of similarity measures in life science is ever paramount given the modern biotechnology in producing and storing biomedical datasets in large amounts. This paper presents a novel scheme in measuring similarity of two datasets by prediction class, namely SPC. SPC offers an alternative approach to traditionally used ones such as pairwise correlations which assume every attribute carries equal importance. The unique advantage of SPC is the use of a machine learning model called Fuzzy Unordered Rule Induction to infer the similarity between two datasets based on their common attributes and their degrees of relevance pertaining to a predicted class. The method is demonstrated by a case of comparing lung cancer dataset and heart disease dataset
Ching-hsue Cheng - One of the best experts on this subject based on the ideXlab platform.
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fuzzy time series model based on rough set Rule Induction for forecasting stock price
Neurocomputing, 2018Co-Authors: Ching-hsue Cheng, Junhe YangAbstract:Abstract The stock price prediction is an important issue in stock markets because it will result in significant benefits and impacts for investor. In contrast to traditional time series, fuzzy time series can solve the forecast problem with historical data of linguistic values. In order to improve forecast performance of fuzzy time-series models, this study replaced fuzzy logical relationships with Rule-based algorithm to extract forecast Rules from time-series observations. Therefore, this paper developed a novel fuzzy time-series model based on rough set Rule Induction for forecasting stock index, and this study has four contributions to improve forecast accuracy and provide investment point (in right time) to investors: (1) Proposed a novel fuzzy time-series model to improve forecast accuracy, (2) rough sets are employed to generate forecasting Rules to replace fuzzy logical relationship Rules based on the lag period, (3) utilized adaptive expectation model to strengthen forecasting performance, and based on the meaning of adaptive parameter to observe stock fluctuation and oscillation, and (4) proposed buy and sell Rules to calculate the profit and based on three different scenarios to provide investment suggestion to investor as references. For evaluating the proposed model, we practically collected TAIEX, Nikkei, and HSI stock price from 1998 to 2012 years as experimental dataset, and compared the listing models under three error indexes and profits criteria. The results show that the proposed method outperforms listing models in error indexes and profits.
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rough set Rule Induction to build fuzzy time series model in forecasting stock price
Fuzzy Systems and Knowledge Discovery, 2015Co-Authors: Ching-hsue Cheng, Junhe YangAbstract:The investment research of stock is an hot issue because it will bring significant returns for investor. The advantages of fuzzy time series can solve the forecast problem in data with linguistic expression. In order to improve fuzzy time-series models, this study direct replaced fuzzy logic relationships with Rule-based method to extract fuzzy forecast Rules from time series observations. Therefore this paper proposed a Rule Induction based fuzzy time series model to forecast stock index. In summary, there are four refinements to improve accuracy of forecast: (1) use Miller's magic seven (plus or minus two) to determine the lengths of linguistic intervals, (2) utilize LEM2 algorithm to generate forecast Rules, (3) defuzzify the forecast interval based on the generated Rules, and (4) use adaptive expectation model to strengthen forecasting performance. For evaluating the proposed model, the practically collected TAIEX stock price from 1998 to 2006 years are used as experimental dataset, and Chen's model, Yu's model, stepwise regression based on ANFIS, and stepwise regression based on support vector regression are compared with the proposed model in RMSE (root mean square error) and profits criteria. The results express that proposed method holds good performance in accuracy.
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fuzzy time series model based on probabilistic approach and rough set Rule Induction for empirical research in stock markets
Data and Knowledge Engineering, 2008Co-Authors: Hia Jong Teoh, Ching-hsue Cheng, Jrshian ChenAbstract:This study proposes a hybrid fuzzy time series model with two advanced methods, cumulative probability distribution approach (CPDA) and rough set Rule Induction, to forecast stock markets. To improve forecasting accuracy, three refining processes of fuzzy time series are provided in the proposed model: (1) using CPDA to discretize the observations in training datasets based on the characteristics of data distribution, (2) generating Rules (fuzzy logical relationships) by rough set algorithm and (3) producing forecasting results based on Rule support values from rough set algorithm. To verify the forecasting performance of the proposed model in detail, two empirical stock markets (TAIEX and NYSE) are used as evaluating databases; two other methodologies, proposed by Chen and Yu, are used as comparison models, and two different evaluation methods (moving windows) are used. The proposed model shows a greatly improved performance in stock market forecasting compared to other fuzzy time series models.
Junhe Yang - One of the best experts on this subject based on the ideXlab platform.
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fuzzy time series model based on rough set Rule Induction for forecasting stock price
Neurocomputing, 2018Co-Authors: Ching-hsue Cheng, Junhe YangAbstract:Abstract The stock price prediction is an important issue in stock markets because it will result in significant benefits and impacts for investor. In contrast to traditional time series, fuzzy time series can solve the forecast problem with historical data of linguistic values. In order to improve forecast performance of fuzzy time-series models, this study replaced fuzzy logical relationships with Rule-based algorithm to extract forecast Rules from time-series observations. Therefore, this paper developed a novel fuzzy time-series model based on rough set Rule Induction for forecasting stock index, and this study has four contributions to improve forecast accuracy and provide investment point (in right time) to investors: (1) Proposed a novel fuzzy time-series model to improve forecast accuracy, (2) rough sets are employed to generate forecasting Rules to replace fuzzy logical relationship Rules based on the lag period, (3) utilized adaptive expectation model to strengthen forecasting performance, and based on the meaning of adaptive parameter to observe stock fluctuation and oscillation, and (4) proposed buy and sell Rules to calculate the profit and based on three different scenarios to provide investment suggestion to investor as references. For evaluating the proposed model, we practically collected TAIEX, Nikkei, and HSI stock price from 1998 to 2012 years as experimental dataset, and compared the listing models under three error indexes and profits criteria. The results show that the proposed method outperforms listing models in error indexes and profits.
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rough set Rule Induction to build fuzzy time series model in forecasting stock price
Fuzzy Systems and Knowledge Discovery, 2015Co-Authors: Ching-hsue Cheng, Junhe YangAbstract:The investment research of stock is an hot issue because it will bring significant returns for investor. The advantages of fuzzy time series can solve the forecast problem in data with linguistic expression. In order to improve fuzzy time-series models, this study direct replaced fuzzy logic relationships with Rule-based method to extract fuzzy forecast Rules from time series observations. Therefore this paper proposed a Rule Induction based fuzzy time series model to forecast stock index. In summary, there are four refinements to improve accuracy of forecast: (1) use Miller's magic seven (plus or minus two) to determine the lengths of linguistic intervals, (2) utilize LEM2 algorithm to generate forecast Rules, (3) defuzzify the forecast interval based on the generated Rules, and (4) use adaptive expectation model to strengthen forecasting performance. For evaluating the proposed model, the practically collected TAIEX stock price from 1998 to 2006 years are used as experimental dataset, and Chen's model, Yu's model, stepwise regression based on ANFIS, and stepwise regression based on support vector regression are compared with the proposed model in RMSE (root mean square error) and profits criteria. The results express that proposed method holds good performance in accuracy.
