Abstract: A generation device that generates a feature amount range visualization element that defines a continuous feature amount range for each of the factors based on time series data including feature amounts of different factors existing in time series and generates an inter-feature amount visualization element that defines relevance between a first feature amount of a first factor and a second feature amount of a second factor that are continuous in time. The device also generates visualization information indicating a relationship of the feature amounts related to the plurality of different factors by associating, by the inter-feature amount visualization element, a first feature amount range visualization element of the first factor with a second feature amount range visualization element of the second factor.

Abstract: Signal processing assists in searching for a mechanism through a generation equation of a signal for stratifying a patient, which refers to classification of patients suffering from a disease to enable medical treatment.

Abstract: Aspects relate to providing a counterfactual inference management technique capable of providing increased flexibility to allow users to select an appropriate counterfactual inference and offering scalability for handling tabular data and image data in a single configuration. A counterfactual inference management device comprising a classifier unit trained to determine whether a set of input data that includes a set of data features achieves a predetermined target and a counterfactual inference unit for generating a set of transformed data in which a subset of the set of data features are modified to counterfactual features. The classifier unit processes the set of transformed data to determine whether it achieves the predetermined target and calculates a counterfactual loss. The counterfactual inference unit is trained to reduce the counterfactual loss and generate a set of transformed data including counterfactual features that achieve the predetermined target.

Abstract: A data processing apparatus stores an analysis target data group having values of respective variables in a variable group and a value of an objective variable per analysis target, and an element group that the variable group and each of one or more modulation method(s) for modulating the variable(s) are set as elements. When the element selected from the element group is acquired, a modulation function of modulating the value of the variable which is contained in the acquired element is planned on the basis of the history of the acquired element; and the value of the variable per the analysis target is modulated on the basis of the modulation function. Image data are generated which gives a point of coordinates which are values of the modulation result and the objective variable defined by a first axis and a second axis, respectively, per the analysis target.

Abstract: A data analysis apparatus executes: a selection process selecting a first feature variable group that is a trivial feature variable group contributing to prediction and a second feature data group other than the first feature variable group from a set of feature variables; an operation process operating a first regularization coefficient related to a first weight parameter group corresponding to the first feature variable group in a manner that the loss function is larger, and operating a second regularization coefficient related to a second weight parameter group corresponding to the second feature variable group in a manner that the loss function is smaller, among a set of weight parameters configuring a prediction model, in a loss function related to a difference between a prediction result output in a case of inputting the set of feature variables to the prediction model and ground truth data corresponding to the feature variables.

Abstract: To infer a value of an assignment variable with high accuracy. In a causal relation inference device including a processor configured to execute a program and a storage device storing the program, the processor is configured to execute a first calculation process of calculating an internal vector based on a feature vector of a plurality of samples and a first learning parameter, a second calculation process of calculating a reallocation vector based on a second learning parameter and the internal vector calculated by the first calculation process, and a third calculation process of calculating a pointwise weight vector for each of the plurality of samples based on a third learning parameter and the reallocation vector calculated by the second calculation process.

Abstract: The analyzing apparatus: generates first internal data; converts a position of first feature data in a feature space, based on the first internal data and a second learning parameter; reallocates, based on a result of first conversion and the first feature data, the first feature data to a position obtained through the conversion in the feature space; calculates a predicted value of a hazard function of analysis time in a case where the first feature data is given, based on a result of reallocation and a third learning parameter; optimizes the first to third learning parameters, based on a response variable and a first predicted value; generates second internal data, based on second feature data and the optimized first learning parameter; converts a position of the second feature data in the feature space, based on the second internal data and the optimized second learning parameter; and calculates importance data.

Abstract: To present a relationship among the factors F at different time points using continuous feature amounts. A generation device executes: visualization element generation processing, based on time series data including feature amounts of a plurality of different factors existing in time series, a feature amount range visualization element that defines a continuous feature amount range for each of the factors, and generating an inter-feature amount visualization element that defines relevance between a first feature amount of a first factor and a second feature amount of a second factor that are continuous in time, and visualization element generation processing of generating visualization information indicating a relationship of the feature amounts related to the plurality of different factors by associating, by the inter-feature amount visualization element, a first feature amount range visualization element of the first factor with a second feature amount range visualization element of the second factor.

Abstract: A data processing apparatus includes: a storage section storing an object-to-be-analyzed data group having factors and an objective variable per object to be analyzed; a first modulation section modulating a first factor and outputting a first modulation result per object to be analyzed; a second modulation section modulating a second factor and outputting a second modulation result per object to be analyzed; and a generation section that assigns, per object to be analyzed, a coordinate point representing the first modulation result from the first modulation section and the second modulation result from the second modulation section to a coordinate space specified by a first axis corresponding to the first factor and a second axis corresponding to the second factor, and that generates first image data obtained by assigning information associated with the objective variable of the object to be analyzed corresponding to the coordinate point to the coordinate point.

Abstract: A data analysis apparatus executes: a selection process selecting a first feature variable group that is a trivial feature variable group contributing to prediction and a second feature data group other than the first feature variable group from a set of feature variables; an operation process operating a first regularization coefficient related to a first weight parameter group corresponding to the first feature variable group in a manner that the loss function is larger, and operating a second regularization coefficient related to a second weight parameter group corresponding to the second feature variable group in a manner that the loss function is smaller, among a set of weight parameters configuring a prediction model, in a loss function related to a difference between a prediction result output in a case of inputting the set of feature variables to the prediction model and ground truth data corresponding to the feature variables.

Abstract: The analyzing apparatus: generates first internal data; converts a position of first feature data in a feature space, based on the first internal data and a second learning parameter; reallocates, based on a result of first conversion and the first feature data, the first feature data to a position obtained through the conversion in the feature space; calculates a predicted value of a hazard function of analysis time in a case where the first feature data is given, based on a result of reallocation and a third learning parameter; optimizes the first to third learning parameters, based on a response variable and a first predicted value; generates second internal data, based on second feature data and the optimized first learning parameter; converts a position of the second feature data in the feature space, based on the second internal data and the optimized second learning parameter; and calculates importance data.

Abstract: A time series data analysis apparatus: generates first internal data, based on first feature data groups, first internal parameter, and first learning parameter; transforms first feature data's position in a feature space, based on the first internal data and second learning parameter; reallocates the first feature data, based on a first transform result and first feature data groups; calculates a first predicted value, based on a reallocation result and third learning parameter; optimizes the first-third learning parameters by statistical gradient, based on a response variable and first predicted value; generates second internal data, based on second feature data groups, second internal parameter, and optimized first learning parameter; transforms the second feature data's position in a feature space, based on the second internal data and optimized second learning parameter; and calculates importance data for the second feature data, based on a second transform result and optimized third learning parameter.