ERROR DETERMINATION APPARATUS, ERROR DETERMINATION METHOD AND PROGRAM
An error determination device comprising: a classification estimation process observation unit that acquires data in an estimation process from a classification estimation unit that estimates classification of data to be classified, and generates an estimation process feature vector on a basis of the data; a probability estimation unit that generates an estimated probability vector including probabilities each of which is a probability that the data to be classified belongs to one of classes on a basis of the estimation process feature vector; and an error determination unit that determines whether a classification result by the classification estimation unit is correct or incorrect on a basis of the estimated probability vector, and outputs the classification result, a determination result as to whether the classification result is correct or incorrect, and the estimated probability vector.
1 . An error determination device comprising a processor configured to execute operations comprising:
acquiring, an estimated classification of data to be classified;
generating an estimation process feature vector on a basis of the data;
generating an estimated probability vector, wherein the estimated probability vector indicates probabilities each of which is a probability that the data to be classified belongs to one of classes on a basis of the estimation process feature vector;
determining whether a classification result of the estimated classification of data is correct or incorrect on a basis of the estimated probability vector;
outputting the classification result, a determination result indicating whether the classification result is correct or incorrect, and the estimated probability vector.
2 . The error determination device according to claim 1 , wherein
the generating an estimated probability vector uses a machine learning model learned by using a ratio in classification of each piece of learning data into the classes as correct answer data, the ratio being acquired during learning of estimating classification of data.
3 . The error determination device according to claim 1 , wherein
the determining further comprises determining whether the classification result is correct or incorrect by comparing a maximum value of the estimated probabilities each corresponding to one of the classes in the estimated probability vector with a threshold value.
4 . The error determination device according to claim 1 , wherein
the determining further comprises determining whether the classification result is correct or incorrect by comparing an average information amount of the estimated probabilities each corresponding to one of the classes in the estimated probability vector with a threshold value.
5 . An error determination method executed by a computer, the error determination method comprising:
acquiring an estimated classification of data to be classified;
generating an estimation process feature vector on a basis of the data;
generating an estimated probability vector including probabilities each of which is a probability that the data to be classified belongs to one of classes on a basis of the estimation process feature vector;
determining whether a classification result of the estimated classification estimation of data is correct or incorrect on a basis of the estimated probability vector; and
outputting the classification result, a determination result indicating whether the classification result is correct or incorrect, and the estimated probability vector.
6 . A computer-readable non-transitory recording medium storing a computer-executable program instructions that when executed by a processor cause a computer system to execute operations comprising:
acquiring an estimated classification of data to be classified;
generating an estimation process feature vector on a basis of the data;
generating an estimated probability vector including probabilities each of which is a probability that the data to be classified belongs to one of classes on a basis of the estimation process feature vector;
determining whether a classification result of the estimated classification estimation of data is correct or incorrect on a basis of the estimated probability vector; and
outputting the classification result, a determination result indicating whether the classification result is correct or incorrect, and the estimated probability vector.
7 . The error determination device according to claim 2 , wherein
the determining further comprises determining whether the classification result is correct or incorrect by comparing a maximum value of the estimated probabilities each corresponding to one of the classes in the estimated probability vector with a threshold value.
8 . The error determination device according to claim 2 , wherein
the determining further comprises determining whether the classification result is correct or incorrect by comparing an average information amount of the estimated probabilities each corresponding to one of the classes in the estimated probability vector with a threshold value.
9 . The error determination device according to claim 2 , wherein the machine learning model is learned based on supervised learning.
10 . The error determination method according to claim 5 , wherein
the generating an estimated probability vector uses a machine learning model learned by using a ratio in classification of each piece of learning data into the classes as correct answer data, the ratio being acquired during learning of estimating classification of data.
11 . The error determination method according to claim 5 , wherein
the determining further comprises determining whether the classification result is correct or incorrect by comparing a maximum value of the estimated probabilities each corresponding to one of the classes in the estimated probability vector with a threshold value.
12 . The error determination method according to claim 5 , wherein
the determining further comprises determining whether the classification result is correct or incorrect by comparing an average information amount of the estimated probabilities each corresponding to one of the classes in the estimated probability vector with a threshold value.
13 . The error determination method according to claim 10 , wherein
the determining further comprises determining whether the classification result is correct or incorrect by comparing a maximum value of the estimated probabilities each corresponding to one of the classes in the estimated probability vector with a threshold value.
14 . The error determination method according to claim 10 , wherein
the determining further comprises determining whether the classification result is correct or incorrect by comparing an average information amount of the estimated probabilities each corresponding to one of the classes in the estimated probability vector with a threshold value.
15 . The computer-executable non-transitory recording medium according to claim 6 , wherein
the generating an estimated probability vector uses a machine learning model learned by using a ratio in classification of each piece of learning data into the classes as correct answer data, the ratio being acquired during learning of estimating classification of data.
16 . The computer-executable non-transitory recording medium according to claim 6 , wherein
the determining further comprises determining whether the classification result is correct or incorrect by comparing a maximum value of the estimated probabilities each corresponding to one of the classes in the estimated probability vector with a threshold value.
17 . The computer-executable non-transitory recording medium according to claim 6 , wherein
the determining further comprises determining whether the classification result is correct or incorrect by comparing an average information amount of the estimated probabilities each corresponding to one of the classes in the estimated probability vector with a threshold value.
18 . The computer-executable non-transitory recording medium according to claim 15 , wherein
the determining further comprises determining whether the classification result is correct or incorrect by comparing a maximum value of the estimated probabilities each corresponding to one of the classes in the estimated probability vector with a threshold value.
19 . The computer-executable non-transitory recording medium according to claim 15 , wherein
the determining further comprises determining whether the classification result is correct or incorrect by comparing an average information amount of the estimated probabilities each corresponding to one of the classes in the estimated probability vector with a threshold value.
20 . The computer-executable non-transitory recording medium according to claim 15 , wherein the machine learning model is learned based on supervised learning.