A decision tree generating apparatus includes an information gain calculator and a decision tree generator. When a classification target data set including a plurality of pieces of classification target data respectively having different attributes with attribute values assigned thereto is segmented into subsets in a form of a decision tree, the information gain calculator calculates an amount of entropy reduction on each attribute, and calculates an information gain, based on the amount of reduction in the entropy and reliability of a user's answer responsive to an inquiry asking about the attribute. The decision tree generator successively determines an attribute having a maximum information gain to be a node of the decision tree by recursively iterating the segmentation of the pre-segmentation data set, and generates the decision tree that is to be used to determine an order of the inquiries.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A decision tree generating apparatus comprising an information gain calculator and a decision tree generator, wherein when a classification target data set including a plurality of pieces of classification target data respectively having mutually different attributes with attribute values assigned thereto is hierarchically segmented into a plurality of subsets in a form of a decision tree, the information gain calculator calculates an amount of reduction in entropy of the pre-segmentation classification target data set caused by segmentation on each attribute of each piece of the classification target data included in a pre-segmentation data set, and calculates an information gain when the pre-segmentation data set is segmented in accordance with the attribute value of each attribute, based on the amount of reduction in the entropy and reliability that is an index representing correctness or incorrectness of a user's answer responsive to an inquiry asking about the attribute, and wherein the decision tree generator successively determines an attribute having a maximum information gain to be a node of the decision tree by recursively iterating the segmentation of the pre-segmentation data set in accordance with the attribute value of the attribute having the maximum information gain from among the information gains calculated on the attributes, and generates the decision tree that is to be used to determine an order of inquiries asking about the attributes in order to classify the pieces of classification target data by successively assigning the attribute value of the attribute having the maximum information gain to an edge of the node. 2. The decision tree generating apparatus according to claim 1, further comprising reliability calculator that calculates the reliability on each attribute from user's answer instance data that includes instances of a user's correct answer and a user's incorrect answer in response to an inquiry asking about the attribute, wherein the information gain calculator calculates the information gain obtained when the pre-segmentation data set is segmented on a per attribute basis in accordance with the attribute value of each attribute, based on the reliability calculated by the reliability calculator and the amount of reduction in the entropy. 3. The decision tree generating apparatus according to claim 1, wherein the reliability comprises a correct answer rate of the user's answer responsive to the inquiry asking about the attribute, and wherein the information gain calculator calculates the information gain by multiplying the amount of reduction in the entropy by the correct answer rate. 4. The decision tree generating apparatus according to claim 3, wherein the user's answer instance data includes data concerning an answer time period until the user gives an answer in response to an inquiry, wherein the correct answer rate includes a correct answer rate of an average answer time period of the user determined from a correlation between the answer time period and the correct answer rate, and the information gain calculator calculates the information gain by multiplying the amount of reduction in the entropy by the correct answer rate of the average answer time period. 5. The decision tree generating apparatus according to claim 1, wherein the reliability includes conditional entropy when a specific attribute value that is an answer to an inquiry asking about a specific attribute is acquired, and wherein the information gain calculator calculates the information gain by subtracting the conditional entropy from the amount of reduction in the entropy. 6. The decision tree generating apparatus according to claim 2, wherein the user's answer instance data further includes user information representing an attribute of the user, wherein the reliability calculator calculates the reliability on the attribute of each user from the user's answer instance data, and wherein the information gain calculator calculates the information gain on the attribute of each user, based on the reliability and the amount of reduction in the entropy. 7. The decision tree generating apparatus according to claim 1, wherein the decision tree generator generates a single decision tree from the plurality pieces of classification target data. 8. The decision tree generating apparatus according to claim 1, wherein the decision tree generator sets the classification target data set to be an initial value of the pre-segmentation data set, causes the information gain calculator to calculate the information gain when the pre-segmentation data set that is set in accordance with the attribute value of each attribute is segmented into the subsets, determines the attribute having the maximum information gain to be a node that segments the set pre-segmentation data set, segments the pre-segmentation data set into the subsets in accordance with the attribute value of the attribute having the maximum information gain, marks the attribute having the maximum information gain on each of the subsets, newly sets each of the subsets to be the pre-segmentation data set, recursively iterates the calculating, the determining, the segmenting, and the marking on the newly set pre-segmentation data, and causes the information gain calculator to calculate the information gain when the set pre-segmentation data set is segmented in accordance with the attribute value of an attribute, which has not been marked, from among the attributes. 9. An inquiry system that generates classification results from a user's answer to an inquiry, comprising: a memory that stores a decision tree that is generated by the decision tree generating apparatus according to claim 1; an inquirer that outputs an inquiry in accordance with the decision tree stored on the memory; an acquirer that acquires the user's answer responsive to the inquiry from the inquirer; and a generator that generates the classification results responsive to the user's answer acquired by the acquirer. 10. The inquiry system according to claim 9, further comprising a history memory that stores inquiry order information representing an order of inquiries by the inquirer, attribute identification information that identifies an attribute corresponding to an inquiry by the inquirer, an attribute value that is an answer to the inquiry by the inquirer, and reliability on the attribute corresponding to the inquiry by the inquirer, wherein the generator, after generating the classification results, makes an inquiry again asking about an attribute having reliability lower in correctness than a specific value by referencing the history memory. 11. A decision tree generating method of a decision tree generating apparatus, comprising: when a classification target data set including a plurality of pieces of classification target data respectively having mutually different attributes with attribute values assigned thereto is hierarchically segmented into a plurality of subsets in a form of a decision tree, calculating an amount of reduction in entropy of the pre-segmentation classification target data set caused by segmentation on each attribute of each piece of the classification target data included in a pre-segmentation data set, and calculating an information gain when the pre-segmentation data set is segmented in accordance with the attribute value of each attribute, based on the amount of reduction in the entropy and reliability that is an index representing correctness or incorrectness of a user's answer responsive to an inquiry asking about the attribute, and successively determining an attribute having a maximum information gain to be a node of the decision tree by recursively iterating the segmentation of the pre-segmentation data set in accordance with the attribute value of the attribute having the maximum information gain from among the information gains calculated on the attributes, and generating the decision tree that is to be used to determine an order of inquiries asking about the attributes in order to classify the pieces of classification target data by successively assigning the attribute value of the attribute having the maximum information gain to an edge of the node. 12. A non-transitory computer-readable recording medium storing a program causing a computer to execute a process for generating a decision tree, the process comprising: when a classification target data set including a plurality of pieces of classification target data respectively having mutually different attributes with attribute values assigned thereto is hierarchically segmented into a plurality of subsets in a form of a decision tree, calculating an amount of reduction in entropy of the pre-segmentation classification target data set caused by segmentation on each attribute of each piece of the classification target data included in a pre-segmentation data set, and calculating an information gain when the pre-segmentation data set is segmented in accordance with the attribute value of each attribute, based on the amount of reduction in the entropy and reliability that is an index representing correctness or incorrectness of a user's answer responsive to an inquiry asking about the attribute, and successively determining an attribute having a maximum information gain to be a node of the decision tree by recursively iterating the segmentation of the pre-segmentation data set in accordance with the attribute value of the attribute having the maximum information gain from among the information gains calculated on the attributes, and generating the decision tree that is to be used to determine an order of inquiries asking about the attributes in order to classify the pieces of classification target data by successively assigning the attribute value of the attribute having the maximum information gain to an edge of the node.