Predictive model evaluation means 81 evaluates closeness in property between a relearned predictive model and a pre-relearning predictive model. Predictive model updating means 82 updates the pre-relearning predictive model with the relearned predictive model, in the case where the closeness in property meets closeness prescribed by a predetermined condition. The predictive model evaluation means 81 evaluates closeness in prediction result or structural closeness, as the closeness in property of the predictive model.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A predictive model updating system comprising: hardware including a processor; a predictive model evaluation unit implemented at least by the hardware and which evaluates closeness in property between a relearned predictive model and a pre-relearning predictive model; and a predictive model updating unit implemented at least by the hardware and which updates the pre-relearning predictive model with the relearned predictive model, in the case where the closeness in property meets closeness prescribed by a predetermined condition, wherein the predictive model evaluation unit evaluates closeness in structure of the relearned predictive model and structure of the pre-relearning predictive model, as the closeness in property of the predictive model. 2. The predictive model updating system according to claim 1, comprising: a predictive model extraction unit implemented at least by the hardware and which extracts a predictive model meeting a condition prescribed by a rule for determining whether or not to relearn the predictive model, from among a plurality of predictive models; and a predictive model relearning unit implemented at least by the hardware and which relearns the extracted predictive model, wherein the predictive model evaluation unit evaluates the closeness in property between the relearned predictive model obtained by the predictive model relearning unit and the pre-relearning predictive model. 3. The predictive model updating system according to claim 1, wherein the pre-relearning predictive model and the relearned predictive model are a predictive model whose component used for prediction of a sample of a prediction target is determined according to contents of the sample, and wherein the predictive model evaluation unit evaluates the closeness in property of the predictive model, based on a degree of disorder between the component determined in the pre-relearning predictive model and the component determined in the relearned predictive model for the sample of the prediction target. 4. (canceled) 5. The predictive model updating system according to claim 1, wherein the predictive model evaluation unit evaluates a degree of overlap between an attribute used in the pre-relearning predictive model and an attribute used in the relearned predictive model, as the closeness in property of the predictive model. 6. The predictive model updating system according to claim 1, wherein the predictive model evaluation unit evaluates a proportion of sample points commonly classified in the relearned predictive model to a set of sample points commonly classified in the pre-relearning predictive model, as the closeness in property of the predictive model. 7. A predictive model updating method performed by a computer, comprising: evaluating closeness in property between a relearned predictive model and a pre-relearning predictive model; and updating the pre-relearning predictive model with the relearned predictive model, in the case where the closeness in property meets closeness prescribed by a predetermined condition, wherein in the evaluation of the closeness in property, the computer evaluates closeness in structure of the relearned predictive model and structure of the pre-relearning predictive model, as the closeness in property of the predictive model. 8. The predictive model updating method according to claim 7, comprising: extracting a predictive model meeting a condition prescribed by a rule for determining whether or not to relearn the predictive model, from among a plurality of predictive models; and relearning the extracted predictive model, wherein in the evaluation of the closeness in property, the computer evaluates the closeness in property between the relearned predictive model obtained and the pre-relearning predictive model. 9. A non-transitory computer readable information recording medium storing a predictive model updating program, when executed by a processor, that performs a method for: evaluating closeness in property between a relearned predictive model and a pre-relearning predictive model; and updating the pre-relearning predictive model with the relearned predictive model, in the case where the closeness in property meets closeness prescribed by a predetermined condition, wherein in the evaluation of the closeness in property, evaluating closeness in structure of the relearned predictive model and structure of the pre-relearning predictive model, as the closeness in property of the predictive model. 10. The non-transitory computer-readable recording medium according to claim 9, comprising: extracting a predictive model meeting a condition prescribed by a rule for determining whether or not to relearn the predictive model, from among a plurality of predictive models; and relearning the extracted predictive model, wherein in the evaluation of the closeness in property, evaluating the closeness in property between the relearned predictive model obtained and the pre-relearning predictive model. 11. A predictive model updating system comprising: hardware including a processor; a predictive model evaluation unit implemented at least by the hardware and which evaluates closeness in property between a relearned predictive model and a pre-relearning predictive model; and a predictive model updating unit implemented at least by the hardware and which updates the pre-relearning predictive model with the relearned predictive model, in the case where the closeness in property meets closeness prescribed by a predetermined condition, wherein the predictive model evaluation unit evaluates closeness in prediction result of the relearned predictive model and prediction result of the pre-relearning predictive model, as the closeness in property of the predictive model. 12. The predictive model updating system according to claim 11, a predictive model extraction unit implemented at least by the hardware and which extracts a predictive model meeting a condition prescribed by a rule for determining whether or not to relearn the predictive model, from among a plurality of predictive models; and a predictive model relearning unit implemented at least by the hardware and which relearns the extracted predictive model, wherein the predictive model evaluation unit evaluates the closeness in property between the relearned predictive model obtained by the predictive model relearning unit and the pre-relearning predictive model. 13. The predictive model updating system according to claim 11, wherein the pre-relearning predictive model and the relearned predictive model are a predictive model whose component used for prediction of a sample of a prediction target is determined according to contents of the sample, and wherein the predictive model evaluation unit evaluates the closeness in property of the predictive model, based on a degree of disorder between the component determined in the pre-relearning predictive model and the component determined in the relearned predictive model for the sample of the prediction target. 14. A predictive model updating method performed by a computer, comprising: evaluating closeness in property between a relearned predictive model and a pre-relearning predictive model; and updating the pre-relearning predictive model with the relearned predictive model, in the case where the closeness in property meets closeness prescribed by a predetermined condition, wherein in the evaluation of the closeness in property, the computer evaluates closeness in prediction result of the relearned predictive model and prediction result of the pre-relearning predictive model, as the closeness in property of the predictive model. 15. A non-transitory computer readable information recording medium storing a predictive model updating program, when executed by a processor, that performs a method for: evaluating closeness in property between a relearned predictive model and a pre-relearning predictive model; and updating the pre-relearning predictive model with the relearned predictive model, in the case where the closeness in property meets closeness prescribed by a predetermined condition, wherein in the evaluation of the closeness in property, evaluating closeness in prediction result of the relearned predictive model and prediction result of the pre-relearning predictive model, as the closeness in property of the predictive model.