The Experts below are selected from a list of 31707 Experts worldwide ranked by ideXlab platform
Hiroshi Sakai - One of the best experts on this subject based on the ideXlab platform.
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nis apriori based Rule Generation with three way decisions and its application system in sql
Information Sciences, 2020Co-Authors: Hiroshi Sakai, Michinori Nakata, Junzo WatadaAbstract:Abstract In the study, non-deterministic information systems-Apriori-based (NIS-Apriori-based) Rule Generation from table data sets with incomplete information, SQL implementation, and the unique characteristics of the new framework are presented. Additionally, a few unsolved new research topics are proposed based on the framework. We follow the framework of NISs and propose certain Rules and possible Rules based on possible world semantics. Although each Rule τ depends on a large number of possible tables, we prove that each Rule τ is determined by examining only two τ-dependent possible tables. The NIS-Apriori algorithm is an adjusted Apriori algorithm that can handle such tables. Furthermore, it is logically sound and complete with regard to the Rules. Subsequently, the implementation of the NIS-Apriori algorithm in SQL is described and a few new topics induced by effects of NIS-Apriori-based Rule Generation are confirmed. One of the topics that are considered is the possibility of estimating missing values via the obtained certain Rules. The proposed methodology and the environment yielded by NIS-Apriori-based Rule Generation in SQL are useful for table data analysis with three-way decisions.
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on apriori based Rule Generation in sql a case of the deterministic information system
Soft Computing, 2016Co-Authors: Chenxi Liu, Hiroshi Sakai, Xiaoxin Zhu, Michinori NakataAbstract:We have proposed a framework named Rough Non-deterministic Information Analysis (RNIA), and developed two software tools, RNIA in Prolog and getRNIA in Python. In order to handle big data sets, we newly employ SQL and PHP. This paper reports the current state of the software tool in SQL, which is the preliminary version for NIS-Apriori in SQL.
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apriori based Rule Generation in incomplete information databases and non deterministic information systems
Fundamenta Informaticae, 2014Co-Authors: Hiroshi Sakai, Michinori NakataAbstract:This paper discusses issues related to incomplete information databases and considers a logical framework for Rule Generation. In our approach, a Rule is an implication satisfying specified constraints. The term incomplete information databases covers many types of inexact data, such as non-deterministic information, data with missing values, incomplete information or interval valued data. In the paper, we start by defining certain and possible Rules based on non-deterministic information. We use their mathematical properties to solve computational problems related to Rule Generation. Then, we reconsider the NIS-Apriori algorithm which generates a given implication if and only if it is either a certain Rule or a possible Rule satisfying the constraints. In this sense, NIS-Apriori is logically sound and complete. In this paper, we pay a special attention to soundness and completeness of the considered algorithmic framework, which is not necessarily obvious when switching from exact to inexact data sets. Moreover, we analyze different types of non-deterministic information corresponding to different types of the underlying attributes, i.e., value sets for qualitative attributes and intervals for quantitative attributes, and we discuss various approaches to construction of descriptors related to particular attributes within the Rules' premises. An improved implementation of NIS-Apriori and some demonstrations of an experimental application of our approach to data sets taken from the UCI machine learning repository are also presented. Last but not least, we show simplified proofs of some of our theoretical results.
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a nis apriori based Rule generator in prolog and its functionality for table data
Lecture Notes in Computer Science, 2011Co-Authors: Hiroshi Sakai, Michinori Nakata, Dominik ślezakAbstract:Rough Non-deterministic Information Analysis (RNIA) is a rough set based framework for handling several kinds of incomplete information. In our previous research on RNIA, we gave definitions according to two modal concepts, the certainty and the possibility, and thoroughly investigated their mathematical properties. For Rule Generation in RNIA, we proposed NIS-Apriori algorithm, which is an extended Apriori algorithm. Our previous implementation of NIS-Apriori in C suffered from a lack of clarity caused by difficulties in expressing non-deterministic information by procedural languages. Therefore, we recently decided to improve the algorithm's design and re-implement it in Prolog. This paper reports the current state of our algorithmic framework and outlines some new aspects of its functionality.
Sushmita Mitra - One of the best experts on this subject based on the ideXlab platform.
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neurofuzzy classification and Rule Generation of modes of radiowave propagation
IEEE Transactions on Antennas and Propagation, 2003Co-Authors: S Choudhury, Sushmita Mitra, Sankar K PalAbstract:This paper describes, in a neurofuzzy framework, a method for the classification of different modes of radiowave propagation, followed by Generation of linguistic Rules justifying a decision. Weight decay during neural learning helps in imposing a structure on the network, resulting in the extraction of logical Rules. Use of linguistic terms at the input enables better human interpretation of the inferred Rules. The effectiveness of the system is demonstrated on radiosonde data of four different seasons in India.
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rough fuzzy mlp modular evolution Rule Generation and evaluation
IEEE Transactions on Knowledge and Data Engineering, 2003Co-Authors: Sushmita Mitra, Pabitra MitraAbstract:A methodology is described for evolving a Rough-fuzzy multi layer perceptron with modular concept using a genetic algorithm to obtain a structured network suitable for both classification and Rule extraction. The modular concept, based on "divide and conquer" strategy, provides accelerated training and a compact network suitable for generating a minimum number of Rules with high certainty values. The concept of variable mutation operator is introduced for preserving the localized structure of the constituting knowledge-based subnetworks, while they are integrated and evolved. Rough set dependency Rules are generated directly from the real valued attribute table containing fuzzy membership values. Two new indices viz., "certainty" and "confusion" in a decision are defined for evaluating quantitatively the quality of Rules. The effectiveness of the model and the Rule extraction algorithm is extensively demonstrated through experiments alongwith comparisons.
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neuro fuzzy Rule Generation survey in soft computing framework
IEEE Transactions on Neural Networks, 2000Co-Authors: Sushmita Mitra, Yoichi HayashiAbstract:The present article is a novel attempt in providing an exhaustive survey of neuro-fuzzy Rule Generation algorithms. Rule Generation from artificial neural networks is gaining in popularity in recent times due to its capability of providing some insight to the user about the symbolic knowledge embedded within the network. Fuzzy sets are an aid in providing this information in a more human comprehensible or natural form, and can handle uncertainties at various levels. The neuro-fuzzy approach, symbiotically combining the merits of connectionist and fuzzy approaches, constitutes a key component of soft computing at this stage. To date, there has been no detailed and integrated categorization of the various neuro-fuzzy models used for Rule Generation. We propose to bring these together under a unified soft computing framework. Moreover, we include both Rule extraction and Rule refinement in the broader perspective of Rule Generation. Rules learned and generated for fuzzy reasoning and fuzzy control are also considered from this wider viewpoint. Models are grouped on the basis of their level of neuro-fuzzy synthesis. Use of other soft computing tools like genetic algorithms and rough sets are emphasized. Rule Generation from fuzzy knowledge-based networks, which initially encode some crude domain knowledge, are found to result in more refined Rules. Finally, real-life application to medical diagnosis is provided.
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neuro fuzzy pattern recognition methods in soft computing
1999Co-Authors: Sankar K Pal, Sushmita MitraAbstract:From the Publisher: The authors consolidate a wealth of information previously scattered in disparate articles, journals, and edited volumes, explaining both the theory of neuro-fuzzy computing and the latest methodologies for performing different pattern recognition tasks in the neuro-fuzzy network - classification, feature evaluation, Rule Generation, knowledge extraction, and hybridization. Special emphasis is given to the integration of neuro-fuzzy methods with rough sets and genetic algorithms (GAs) to ensure more efficient recognition systems.
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fuzzy multi layer perceptron inferencing and Rule Generation
IEEE Transactions on Neural Networks, 1995Co-Authors: Sushmita Mitra, Sankar K PalAbstract:A connectionist expert system model, based on a fuzzy version of the multilayer perceptron developed by the authors, is proposed. It infers the output class membership value(s) of an input pattern and also generates a measure of certainty expressing confidence in the decision. The model is capable of querying the user for the more important input feature information, if and when required, in case of partial inputs. Justification for an inferred decision may be produced in Rule form, when so desired by the user. The magnitudes of the connection weights of the trained neural network are utilized in every stage of the proposed inferencing procedure. The antecedent and consequent parts of the justificatory Rules are provided in natural forms. The effectiveness of the algorithm is tested on the speech recognition problem, on some medical data and on artificially generated intractable (linearly nonseparable) pattern classes. >
Michinori Nakata - One of the best experts on this subject based on the ideXlab platform.
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nis apriori based Rule Generation with three way decisions and its application system in sql
Information Sciences, 2020Co-Authors: Hiroshi Sakai, Michinori Nakata, Junzo WatadaAbstract:Abstract In the study, non-deterministic information systems-Apriori-based (NIS-Apriori-based) Rule Generation from table data sets with incomplete information, SQL implementation, and the unique characteristics of the new framework are presented. Additionally, a few unsolved new research topics are proposed based on the framework. We follow the framework of NISs and propose certain Rules and possible Rules based on possible world semantics. Although each Rule τ depends on a large number of possible tables, we prove that each Rule τ is determined by examining only two τ-dependent possible tables. The NIS-Apriori algorithm is an adjusted Apriori algorithm that can handle such tables. Furthermore, it is logically sound and complete with regard to the Rules. Subsequently, the implementation of the NIS-Apriori algorithm in SQL is described and a few new topics induced by effects of NIS-Apriori-based Rule Generation are confirmed. One of the topics that are considered is the possibility of estimating missing values via the obtained certain Rules. The proposed methodology and the environment yielded by NIS-Apriori-based Rule Generation in SQL are useful for table data analysis with three-way decisions.
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on apriori based Rule Generation in sql a case of the deterministic information system
Soft Computing, 2016Co-Authors: Chenxi Liu, Hiroshi Sakai, Xiaoxin Zhu, Michinori NakataAbstract:We have proposed a framework named Rough Non-deterministic Information Analysis (RNIA), and developed two software tools, RNIA in Prolog and getRNIA in Python. In order to handle big data sets, we newly employ SQL and PHP. This paper reports the current state of the software tool in SQL, which is the preliminary version for NIS-Apriori in SQL.
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apriori based Rule Generation in incomplete information databases and non deterministic information systems
Fundamenta Informaticae, 2014Co-Authors: Hiroshi Sakai, Michinori NakataAbstract:This paper discusses issues related to incomplete information databases and considers a logical framework for Rule Generation. In our approach, a Rule is an implication satisfying specified constraints. The term incomplete information databases covers many types of inexact data, such as non-deterministic information, data with missing values, incomplete information or interval valued data. In the paper, we start by defining certain and possible Rules based on non-deterministic information. We use their mathematical properties to solve computational problems related to Rule Generation. Then, we reconsider the NIS-Apriori algorithm which generates a given implication if and only if it is either a certain Rule or a possible Rule satisfying the constraints. In this sense, NIS-Apriori is logically sound and complete. In this paper, we pay a special attention to soundness and completeness of the considered algorithmic framework, which is not necessarily obvious when switching from exact to inexact data sets. Moreover, we analyze different types of non-deterministic information corresponding to different types of the underlying attributes, i.e., value sets for qualitative attributes and intervals for quantitative attributes, and we discuss various approaches to construction of descriptors related to particular attributes within the Rules' premises. An improved implementation of NIS-Apriori and some demonstrations of an experimental application of our approach to data sets taken from the UCI machine learning repository are also presented. Last but not least, we show simplified proofs of some of our theoretical results.
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a nis apriori based Rule generator in prolog and its functionality for table data
Lecture Notes in Computer Science, 2011Co-Authors: Hiroshi Sakai, Michinori Nakata, Dominik ślezakAbstract:Rough Non-deterministic Information Analysis (RNIA) is a rough set based framework for handling several kinds of incomplete information. In our previous research on RNIA, we gave definitions according to two modal concepts, the certainty and the possibility, and thoroughly investigated their mathematical properties. For Rule Generation in RNIA, we proposed NIS-Apriori algorithm, which is an extended Apriori algorithm. Our previous implementation of NIS-Apriori in C suffered from a lack of clarity caused by difficulties in expressing non-deterministic information by procedural languages. Therefore, we recently decided to improve the algorithm's design and re-implement it in Prolog. This paper reports the current state of our algorithmic framework and outlines some new aspects of its functionality.
Junzo Watada - One of the best experts on this subject based on the ideXlab platform.
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nis apriori based Rule Generation with three way decisions and its application system in sql
Information Sciences, 2020Co-Authors: Hiroshi Sakai, Michinori Nakata, Junzo WatadaAbstract:Abstract In the study, non-deterministic information systems-Apriori-based (NIS-Apriori-based) Rule Generation from table data sets with incomplete information, SQL implementation, and the unique characteristics of the new framework are presented. Additionally, a few unsolved new research topics are proposed based on the framework. We follow the framework of NISs and propose certain Rules and possible Rules based on possible world semantics. Although each Rule τ depends on a large number of possible tables, we prove that each Rule τ is determined by examining only two τ-dependent possible tables. The NIS-Apriori algorithm is an adjusted Apriori algorithm that can handle such tables. Furthermore, it is logically sound and complete with regard to the Rules. Subsequently, the implementation of the NIS-Apriori algorithm in SQL is described and a few new topics induced by effects of NIS-Apriori-based Rule Generation are confirmed. One of the topics that are considered is the possibility of estimating missing values via the obtained certain Rules. The proposed methodology and the environment yielded by NIS-Apriori-based Rule Generation in SQL are useful for table data analysis with three-way decisions.
Yoichi Hayashi - One of the best experts on this subject based on the ideXlab platform.
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Greedy Rule Generation from discrete data and its use in neural network Rule extraction
Neural Networks, 2008Co-Authors: Koichi Odajima, Yoichi Hayashi, Gong Tianxia, Rudy SetionoAbstract:This paper proposes a GRG (Greedy Rule Generation) algorithm, a new method for generating classification Rules from a data set with discrete attributes. The algorithm is ''greedy'' in the sense that at every iteration, it searches for the best Rule to generate. The criteria for the best Rule include the number of samples and the size of subspaces that it covers, as well as the number of attributes in the Rule. This method is employed for extracting Rules from neural networks that have been trained and pruned for solving classification problems. The classification Rules are extracted from the neural networks using the standard decompositional approach. Neural networks with one hidden layer are trained and the proposed GRG algorithm is applied to their discretized hidden unit activation values. Our experimental results show that neural network Rule extraction with the GRG method produces Rule sets that are accurate and concise. Application of GRG directly on three medical data sets with discrete attributes also demonstrates its effectiveness for Rule Generation.
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neuro fuzzy Rule Generation survey in soft computing framework
IEEE Transactions on Neural Networks, 2000Co-Authors: Sushmita Mitra, Yoichi HayashiAbstract:The present article is a novel attempt in providing an exhaustive survey of neuro-fuzzy Rule Generation algorithms. Rule Generation from artificial neural networks is gaining in popularity in recent times due to its capability of providing some insight to the user about the symbolic knowledge embedded within the network. Fuzzy sets are an aid in providing this information in a more human comprehensible or natural form, and can handle uncertainties at various levels. The neuro-fuzzy approach, symbiotically combining the merits of connectionist and fuzzy approaches, constitutes a key component of soft computing at this stage. To date, there has been no detailed and integrated categorization of the various neuro-fuzzy models used for Rule Generation. We propose to bring these together under a unified soft computing framework. Moreover, we include both Rule extraction and Rule refinement in the broader perspective of Rule Generation. Rules learned and generated for fuzzy reasoning and fuzzy control are also considered from this wider viewpoint. Models are grouped on the basis of their level of neuro-fuzzy synthesis. Use of other soft computing tools like genetic algorithms and rough sets are emphasized. Rule Generation from fuzzy knowledge-based networks, which initially encode some crude domain knowledge, are found to result in more refined Rules. Finally, real-life application to medical diagnosis is provided.
