Rule generation system and method of generating rule
First Claim
1. A rule generating system for generating rules from training data including a set of specific values related to input and output variables, the rules representing input/output relationships between the input and output variables, the training data including both numeric data and symbol data, comprising:
- division method defining means for defining a division method indicating a number of intervals into which a domain is divided and categorical data expressed by symbols corresponding to the respective intervals for the variables corresponding to the numeric data in the training data;
label presenting means for converting all the numeric data of the training data into the categorical data in accordance with the defined division method, results of the conversion producing an instance table, and for generating division information indicating dividing positions of the domains for the respective numeric data;
rule induction means for extracting rules from the instance table, the rules being expressed by the categorical data;
membership function generating means for generating a membership function using the division information generated by the label presenting means; and
fuzzy rule synthesizing means for synthesizing fuzzy rules from the rules inducted by the rule induction means and the membership function generated by the membership function generating means;
wherein the fuzzy rules synthesized by the fuzzy rule synthesizing means outputs inference results in the categorical data form.
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Abstract
A rule generation apparatus includes a label presenter in which when producing from training data including a set of specific values related to input and output variables rules representing input/output relationships between the input and output variables, numeric data of the training data is converted into categorical data expressed by symbols to generate an instance table, an RI device for extracting rules from the instance table, and a rule converter for converting the extracted rules into fuzzy rules. When the training data is divided to be distributively stored in a plurality of server processors, label assignment is conducted by each server processor such that a client processor later combines instance tables with each other to achieve rule induction and conversion.
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Citations
14 Claims
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1. A rule generating system for generating rules from training data including a set of specific values related to input and output variables, the rules representing input/output relationships between the input and output variables, the training data including both numeric data and symbol data, comprising:
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division method defining means for defining a division method indicating a number of intervals into which a domain is divided and categorical data expressed by symbols corresponding to the respective intervals for the variables corresponding to the numeric data in the training data; label presenting means for converting all the numeric data of the training data into the categorical data in accordance with the defined division method, results of the conversion producing an instance table, and for generating division information indicating dividing positions of the domains for the respective numeric data; rule induction means for extracting rules from the instance table, the rules being expressed by the categorical data; membership function generating means for generating a membership function using the division information generated by the label presenting means; and fuzzy rule synthesizing means for synthesizing fuzzy rules from the rules inducted by the rule induction means and the membership function generated by the membership function generating means; wherein the fuzzy rules synthesized by the fuzzy rule synthesizing means outputs inference results in the categorical data form. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A rule generating system for generating rules from training data including a set of specific values related to input and output variables, the rules representing input/output relationships between the input and output variables, the training data including both numeric data and symbol data, comprising:
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division method defining means for defining a division method indicating a number of intervals into which a domain is divided and categorical data expressed by symbols corresponding to the respective intervals for the variables corresponding to the numeric data in the training data; label presenting means for converting all the numeric data of the training data into the categorical data in accordance with the defined division method, results of the conversion producing an instance table, and for generating division information indicating dividing positions of the domains for the respective numeric data; rule induction means for extracting rules from the instance table, the rules being expressed by the categorical data; membership function generating means for generating a membership function using the division information generated by the label presenting means; and fuzzy rule synthesizing means for synthesizing fuzzy rules from the rules inducted by the rule induction means and the membership function generated by the membership function generating means; wherein the fuzzy rules synthesized by the fuzzy rule synthesizing means outputs inference results in the categorical data form; wherein the label presenting means divides for each of the variables a definition domain of the variable at predetermined points into several intervals, assigns labels respectively to the intervals, and converts numeric data of the training data into categorical data corresponding to an interval to which the numeric data belongs; wherein the label presenting means divides the definition domain at points inputted by the user; and wherein the label presenting means includes; means for displaying a histogram of the numeric data for each of the variables; input means for operating a pointer superimposed onto the presented histogram; and means for indicating decision of the dividing points by the pointer.
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10. A rule generating apparatus for generating rules from training data including a set of specific values related to input and output variables, the rules representing input/output relationships between the input and output variables, the training data including both numeric data and symbol data, comprising:
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a client processor; and a plurality of server processors connected to the client processor; the client processor comprising; division method defining means for defining a division method indicating a number of intervals into which a domain is divided and categorical data expressed by symbols corresponding to the respective intervals for the variables corresponding to the numeric data in the training data; label presenting means for converting all the numeric data of the training data into the categorical data in accordance with the defined division method, results of the conversion producing a label code correspondence table, and for generating division information indicating dividing positions of the domains for the respective numeric data; transmitting means for transmitting the label code correspondence table to the server processors; means for combining instance tables sent from the server processors; a rule induction device for extracting rules from each of the instance tables, the rules being expressed by the categorical data; membership function generating means for generating a membership function using the division information generated by the label presenting means; and fuzzy rule synthesizing means for synthesizing fuzzy rules from the rules inducted by the rule induction means and the membership function generated by the membership function generating means, the fuzzy rules synthesized by the fuzzy rule synthesizing means outputs inference results in the categorical data form; and each of the server processors including; a data base for storing therein a portion of the training data; label presenting means for converting the numeric data and symbol data of the training data into the label codes and thereby producing an instance table; and transmitting means for transmitting the instance table to the client processor. - View Dependent Claims (11, 12)
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13. A rule generating method for generating rules from training data including a set of specific values related to input and output variables, the rules representing input/output relationships between the input and output variables, the training data including both numeric data and symbol data, comprising the steps of:
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defining a division method indicating a number of intervals into which a domain is divided and categorical data expressed by symbols corresponding to the respective intervals for the variables corresponding to the numeric data in the training data; converting all the numeric data of the training data into the categorical data in accordance with the division method set forth in the defining step, results of the conversion producing an instance table; generating division information indicating dividing positions of the domains for the respective numeric data; extracting rules from the instance table, the rules being expressed by the categorical data; generating a membership function using the information generated in the converting step; and synthesizing fuzzy rules from the extracted rules and the generated membership function, the fuzzy rules containing inference results in the categorical data form. - View Dependent Claims (14)
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Specification