METHOD OF GENERATING A CLASSIFICATION MODEL AND CLASSIFICATION METHOD USING SUCH A MODEL
A computer-implemented method for generating a classification model includes: obtaining at least one group of learning data, identifying at least one characteristic to be studied of the learning data, extracting a value of each characteristic defined for all learning data, identifying ranges of values for each characteristic from the extracted values, creating a classification table, assigning a class to each cell of the classification table according to a number of occurrences of the learning data of each group according to their extracted value of each characteristic with respect to the ranges of values defined for each studied characteristic, and generating a classification model comprising the classification table.
1 . A method for generating a classification model, the method comprising:
obtaining at least one group of learning data, each group of learning data being associated with an indicated class;
identifying at least one characteristic to be studied of the learning data;
extracting a value of each characteristic defined for all learning data;
identifying ranges of values for each characteristic from the extracted values;
creating a classification table having a number of dimensions corresponding to the number of studied characteristics, each dimension having a size equal to the number of ranges of values defined for the characteristic associated with this dimension, each cell of the classification table being associated with a range of values of each studied characteristic;
assigning a class to each cell of the classification table according to a number of occurrences of the learning data of each group according to their extracted value of each characteristic with respect to the ranges of values defined for each studied characteristic; and
generating the classification model comprising the classification table.
2 . The method according to claim 1 , wherein the classification model further includes a minimum value and a maximum value of each studied characteristic.
3 . The method according to claim 1 , wherein the class assigned to a cell of the classification table corresponds to the class of the group of learning data having the largest number of occurrences of learning data over the range of values of each characteristic associated with the cell of the classification table.
4 . The method according to claim 1 , wherein the class assigned to a cell of the classification table corresponds to the class having the highest probability compared to the classes assigned to the adjacent cells of the classification table if the number of occurrences of the learning data over the range of values of each characteristic associated with the cell is the same and non-zero for each group of learning data.
5 . The method according to claim 1 , wherein the class assigned to a cell of the classification table corresponds to an undetermined class or to the class having the highest probability compared to the classes assigned to the adjacent cells of the classification table if the number of occurrences of the learning data over the range of values of each characteristic associated with the cell is zero for each group of learning data.
6 . The method according to claim 1 , wherein the ranges of values for each characteristic are set using Sturges's rule.
7 . The method according to claim 1 , further comprising developing a classification computer program product comprising instructions which, when the program is executed by a computer, cause the latter to implement the generated classification model.
8 . A computer system comprising:
a memory comprising a computer program, the computer program comprising instructions to:
obtain at least one group of learning data, each group of learning data being associated with an indicated class;
identify at least one characteristic to be studied of the learning data;
extract a value of each characteristic defined for all learning data;
identify ranges of values for each characteristic from the extracted values;
create a classification table having a number of dimensions corresponding to the number of studied characteristics, each dimension having a size equal to the number of ranges of values defined for the characteristic associated with this dimension, each cell of the classification table being associated with a range of values of each studied characteristic;
assign a class to each cell of the classification table according to a number of occurrences of the learning data of each group according to their extracted value of each characteristic with respect to the ranges of values defined for each studied characteristic; and
generate a classification model comprising the classification table; and
a processor configured to execute the computer program.
9 . The computer system according to claim 8 , wherein the classification model further includes a minimum value and a maximum value of each studied characteristic.
10 . The computer system according to claim 8 , wherein the class assigned to a cell of the classification table corresponds to the class of the group of learning data having the largest number of occurrences of learning data over the range of values of each characteristic associated with the cell of the classification table.
11 . The computer system according to claim 8 , wherein the class assigned to a cell of the classification table corresponds to the class having the highest probability compared to the classes assigned to the adjacent cells of the classification table if the number of occurrences of the learning data over the range of values of each characteristic associated with the cell is the same and non-zero for each group of learning data.
12 . The computer system according to claim 8 , wherein the class assigned to a cell of the classification table corresponds to an undetermined class or to the class having the highest probability compared to the classes assigned to the adjacent cells of the classification table if the number of occurrences of the learning data over the range of values of each characteristic associated with the cell is zero for each group of learning data.
13 . The computer system according to claim 8 , wherein the ranges of values for each characteristic are set using Sturges's rule.
14 . The computer system according to claim 8 , further comprising developing a classification computer program product comprising instructions which, when the program is executed by a computer, cause the latter to implement the generated classification model.
15 . A classification method, the classification method comprising:
obtaining at least one group of learning data, each group of learning data being associated with an indicated class;
identifying at least one characteristic to be studied of the learning data;
extracting a value of each characteristic defined for all learning data;
identifying ranges of values for each characteristic from the extracted values;
creating a classification table having a number of dimensions corresponding to the number of studied characteristics, each dimension having a size equal to the number of ranges of values defined for the characteristic associated with this dimension, each cell of the classification table being associated with a range of values of each studied characteristic;
assigning a class to each cell of the classification table according to a number of occurrences of the learning data of each group according to their extracted value of each characteristic with respect to the ranges of values defined for each studied characteristic;
generating a classification model comprising the classification table;
obtaining data to be classified;
extracting values from the data to be classified for each characteristic defined in the classification model; and
determining the class of the data to be classified from the classification table of the classification model indicating the class assigned for the extracted values.
16 . The method according to claim 15 , wherein the classification model includes a minimum value and a maximum value of each studied characteristic, and wherein the class assigned for the data to be classified is found in the classification table from the values extracted from the data to be classified, the minimum and maximum values of each studied characteristic and the size of each dimension of the classification table.
17 . The method according to claim 15 , wherein the classification model further includes a minimum value and a maximum value of each studied characteristic.
18 . The method according to claim 15 , wherein the class assigned to a cell of the classification table corresponds to the class of the group of learning data having the largest number of occurrences of learning data over the range of values of each characteristic associated with the cell of the classification table.
19 . The method according to claim 15 , wherein the class assigned to a cell of the classification table corresponds to the class having the highest probability compared to the classes assigned to the adjacent cells of the classification table if the number of occurrences of the learning data over the range of values of each characteristic associated with the cell is the same and non-zero for each group of learning data.
20 . The method according to claim 15 , wherein the class assigned to a cell of the classification table corresponds to an undetermined class or to the class having the highest probability compared to the classes assigned to the adjacent cells of the classification table if the number of occurrences of the learning data over the range of values of each characteristic associated with the cell is zero for each group of learning data.