Abstract: A method and apparatus that recommends a feature from a data set. The method includes performing a first conversion on the data set including one target variable and a plurality of feature variables by converting a missing value for each of the feature variables into a preset constant, executing a first algorithm to determine a level of redundancy of the plurality of feature variables, using the data set converted by the first conversion and producing first recommendation information that includes a predetermined number of feature variables selected based on a result of the executing the first algorithm.

Abstract: A method for data analysis according to an embodiment includes acquiring, from a client device, a ciphertext for a precomputation result generated by applying some of a plurality of operations for performing an analysis algorithm based on target data to the target data, and generating an encrypted computation result for remaining operations of the plurality of operations by using the ciphertext.

Abstract: A method for performing an operation according to an embodiment includes assigning an error value to encrypted data; and performing a homomorphic operation for an approximation function, which is obtained by approximating a target function, by using, as an input value, the encrypted data to which the error value is assigned.

Abstract: A method and apparatus for correcting missing values in data are provided. A method of correcting missing values in basic data according to an embodiment includes a data extraction step, a prediction model configuration step, a first correction step, and a second correction step. The method corrects missing values in data by repeating the steps of generating a prediction model for correcting the missing value and correcting the missing value with the use of the prediction model.

Abstract: A hyperparameter optimization method performed by a hyperparameter optimization apparatus to optimize hyperparameters of a model includes calculating an evaluation score for each of a plurality of hyperparameter samples constituting a first hyperparameter sample set by applying each of the hyperparameter samples to a target model, performing weighted clustering of the hyperparameter samples by using the calculated evaluation scores as weights and constructing a second hyperparameter sample set based on the clustering result.

Abstract: A data prediction method based on generative adversarial network (GAN) may be performed by a computing device. The data prediction method may include pre-training a generator neural network based on an auto encoder structure including an encoder and a decoder, and training a GAN including a discriminator neural network and the pre-trained generator neural network by a transfer learning method, wherein training the GAN by a transfer learning method includes training the GAN while fixing a decoder neural network of the generator neural network to the pre-trained initial state.

Abstract: A method and apparatus that recommends a feature from a data set. The method includes performing a first conversion on the data set including one target variable and a plurality of feature variables by converting a missing value for each of the feature variables into a preset constant, executing a first algorithm to determine a level of redundancy of the plurality of feature variables, using the data set converted by the first conversion and producing first recommendation information that includes a predetermined number of feature variables selected based on a result of the executing the first algorithm.

Abstract: A method for data analysis according to an embodiment includes acquiring, from a client device, a ciphertext for a precomputation result generated by applying some of a plurality of operations for performing an analysis algorithm based on target data to the target data, and generating an encrypted computation result for remaining operations of the plurality of operations by using the ciphertext.

Abstract: A method for performing an operation according to an embodiment includes assigning an error value to encrypted data; and performing a homomorphic operation for an approximation function, which is obtained by approximating a target function, by using, as an input value, the encrypted data to which the error value is assigned.

Abstract: A method and apparatus for correcting missing values in data are provided. A method of correcting missing values in basic data according to an embodiment includes a data extraction step, a prediction model configuration step, a first correction step, and a second correction step. The method corrects missing values in data by repeating the steps of generating a prediction model for correcting the missing value and correcting the missing value with the use of the prediction model.

Abstract: A hyperparameter optimization method performed by a hyperparameter optimization apparatus to optimize hyperparameters of a model includes calculating an evaluation score for each of a plurality of hyperparameter samples constituting a first hyperparameter sample set by applying each of the hyperparameter samples to a target model, performing weighted clustering of the hyperparameter samples by using the calculated evaluation scores as weights and constructing a second hyperparameter sample set based on the clustering result.

Abstract: A method of determining an optimal statistical model that can best show the statistical characteristics of given data and an apparatus performing the method are provided. The method acquires target data to be analyzed, where the target data consists of a plurality of independent variables and a dependent variable. Then, the method determines one or more independent variables based on variances in the target data, establishes a first statistical model that shows the relationship between the m independent variables and the dependent variable, and calculates first error of the first statistical model. The method generates a plurality of first statistical models by repeatedly performing the steps of establishing the first statistical model and calculating the first error while changing the value of m, and selects a statistical model with minimum error as an optimal statistical model for the target data.