Fujitsu Corporation

Keisuke Goto

Hiroya Inakoshi

Kotaro Ohori

Hiroaki Iwashita

Tatsuya Asai

Takashi Katoh

Yuiko Ohta

Takuya Takagi

Hiroaki Morikawa

Kento Uemura

Koji Maruhashi

Yoshinobu Shiota

With respect to training data records in which combinations of data item values of data items are individually associated with label information, the data item values are converted based on a criterion per data item into discretized data values. Training processing for training a model that receives the discretized data values as input and performs determination about the label information is performed by using training data records obtained by the conversion. From an execution result of the training processing, feature data records, each of which differently indicates a combination of two or more data items for the determination among the data items, and index values, which indicate importance levels of the feature data records respectively, are acquired. The criterion for the discretization of the data item values is changed based on at least one of the feature data records having been selected based on the index values.

Method and apparatus for training machine learning model using discretization

A non-transitory computer-readable recording medium stores therein a prediction program that causes a computer to execute a process including receiving input data to be predicted, generating a tree structure data based on a plurality of pieces of rule information each indicated by an association of a combination of attribute values of a plurality of attributes with a label according to a predetermined order condition for the plurality of attributes, the tree structure data being obtained by aggregating the plurality of pieces of rule information, the tree structure data including an attribute value as a branch, and determining a degree of contribution to make a determination result on a predetermined value of a predetermined attribute reach a predetermined label based on a likelihood of obtaining a value of the predetermined label as the determination result, when the attribute value of the predetermined attribute is determined to be the predetermined value.

Prediction method, prediction apparatus, and computer-readable recording medium

A non-transitory computer-readable recording medium stores therein a prediction program that causes a computer to execute a process including obtaining a machine learning model trained by using training data, the machine learning model predicting presence or absence of purchase actions or predetermined actions of users corresponding to feature information on the users, the training data including feature information on users and information indicating the presence or the absence of the purchase actions of commercial products or the predetermined actions, receiving input of a budget amount in an entry field displayed on a display in association with a user group including users including a common feature in the feature information, and displaying, in association with the budget amount on the display, feature information on the users included in the user group corresponding to the entry field.

Prediction method, prediction device, and computer-readable recording medium

A non-transitory computer-readable recording medium stores therein an extraction program that causes a computer to execute a process including: generating a plurality of combinations of conditions relating to a plurality of item values included in data; calculating an index value that indicates a degree of cooccurrence between a specified response variable and each of the plurality of combinations, by using a machine learning model that estimates a response variable from the plurality of item values, the machine learning model having been trained by using the data; and extracting a specific combination from among the plurality of combinations based on any one of the condition and the index value.

Extraction method, extraction device, and computer-readable recording medium

A non-transitory computer-readable recording medium stores therein a learning data selection program that causes a computer to execute a process including: extracting a first input data group relating to first input data in correspondence with designation of the first input data included in an input data group input to a machine learning model, the machine learning model classifying or determining transformed data that is transformed from input data; acquiring a first transformed data group of the machine learning model and a first output data group of the machine learning model, respectively, the first transformed data group being input to the machine learning model and corresponding to the first input data group, the first output data group corresponding to the first transformed data group; and selecting learning target data of an estimation model from the first input data group.

Learning data selection method, learning data selection device, and computer-readable recording medium

A non-transitory computer-readable recording medium stores therein a predicting program that causes a computer to execute: receiving input data to be subjected to prediction; and generating, from training data sets each having explanatory variables and an objective variable, a prediction result, by using a hypothesis set and respective weights of hypotheses included in the hypothesis set, the hypotheses each being formed of a combination of the explanatory variables, classifying any of the training data sets and satisfying a specific condition, the weights being learnt based on whether each of the hypotheses holds true for each of the training data sets. The generating includes determining a value of a variable included in a pseudo-Boolean function such that a probability satisfies a predetermined standard, the probability being a probability that the prediction result satisfies the specific condition, the pseudo-Boolean function including variables corresponding to the explanatory variables.

Predicting method, predicting apparatus, and computer-readable recording medium

A server inputs behavior information of a target to a trained machine learning model that learn a plurality of association relations obtained by associating combinations of behaviors generated from a plurality of behaviors included in a plurality of training data items with likelihoods indicating certainties that the combinations of the behaviors are in a specific state, the trained machine learning having been trained by using the plurality of training data items obtained by associating combinations of behaviors of persons corresponding to the specific state. The server specifies a difference between the combination of the behaviors in each of the plurality of association relations and the behavior information of the target based on output results of the trained machine learning model, and determines an additional behavior for causing the target to transit to the specific state based on the likelihood the difference.

Non-transitory computer-readable recording medium, determination method, and information processing apparatus

An optional array in a memory includes an array having blocks each including an address word and a data word, and a boundary that is a position where a ratio between the numbers of unwritten blocks in M area and written blocks in W area is an integer ratio. The controlling process includes when a second write for writing a special value in a written block in the second area is invoked, executing a shrink process of shifting the boundary to shrink the first area; in a case where the first adjacent block at the boundary is a written block, storing an address of the first adjacent block and of a first link destination block forming a link with the write destination block in address words of the first link destination block and of the first adjacent block respectively to form a link.

Array control program, array control method, and array control apparatus

A method of storing target data including a plurality of types of data includes acquiring first information indicating a number of data piece of each of the plurality of types of data, and second information indicating an priority order of the plurality of types of data, storing, in a management area, third information indicating an area defined on the first and the second information within the data storage area, specifying a storage position of first data included in the target data, storing the first data in the specified storage position when second data is not stored in the specified storage position, and storing the first data in a position in the management area in which the third information corresponding to the type of the first data is stored or in the specified storage position, based on the priority order, when the second data is stored in the specified storage position.

Method of storing data, information processing apparatus and non-transitory computer-readable storage medium

An initializable array has a plurality of blocks each having an address word and a data word, a boundary indicative of a two-division position where the plurality of blocks is divided into two divided areas and an initial value for each element of the array is stored, the boundary is a position where a ratio for the number of unwritten blocks in a first area and the number of written blocks in a second area is an integer ratio. An array control program causes a computer to execute shifting the boundary to extend the first area and generating an initialized written block in the first area; in a case where a write destination block is an unwritten block in the second area, forming a link between the initialized written block in the first area and the write destination block; and writing a write value to the write destination block.

A processor obtains a table that contains numerical values or character strings in its cells. The processor then replaces each numerical value with a first constant value, and each character string with a second constant value. The two constant values have opposite signs. The processor generates area datasets each including first to third rectangular areas. The right side of the second rectangular area coincides with the left side of the first rectangular area. The bottom side of the third rectangular area coincides with the top side of the first rectangular area. With respect to each generated area dataset, the processor compares a sum of first and second constant values in the first rectangular area with a sum of first and second constant values in the second and third rectangular areas. The processor outputs at least one of the area datasets according to the comparison result.

Method and apparatus for extracting areas

A data processing method executed by a computer, the data processing method including specifying a first region range among from a data table, a first region range including a plurality of numerical value regions which are continuously disposed in a first direction, a plurality of numerical values in the plurality of numerical value regions having a relationship with a specified numerical value in an adjacent region, specifying a second region range, the second region range being specified by shifting the first region range in a second direction, the second region range including at least one character string region and at least one blank region, associating a character string in the at least one character string region and the plurality of numerical values, and outputting data that indicates an association between the character string in the at least one character string region and the plurality of numerical values.

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Kawasaki, Japan

Keisuke Goto