A feature set determining method includes obtaining, according to a received feature set determining request, data used for feature learning. The feature set determining request includes a learning objective of the feature learning. The method includes performing type analysis on the data to divide the data into first-type data and second-type data. The method includes performing semi-supervised learning on the first-type data to extract multiple first-type features. The method includes performing adaptive learning on the second-type data to extract multiple second-type features. The method includes evaluating the first-type features and the second-type features to obtain an optimal feature set.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A feature set determining method, comprising: obtaining, according to a received feature set determining request, data used for feature learning, wherein the feature set determining request comprises a learning objective of the feature learning; performing type analysis on the data to divide the data into first-type data and second-type data, wherein the first-type data is non-numeric-type data, and wherein the second-type data is numeric-type data; performing semi-supervised learning on the first-type data to extract multiple first-type features; performing adaptive learning on the second-type data to extract multiple second-type features; evaluating the multiple first-type features and the multiple second-type features to obtain an optimal feature set, wherein the optimal feature set comprises multiple features with accuracy rates meeting a preset condition, and wherein the accuracy rate is a degree of closeness between each feature and the learning objective; and outputting the optimal feature set to respond to the feature set determining request. 2. The method according to claim 1, wherein the first-type data comprises labeled data and unlabeled data, and wherein performing the semi-supervised learning on the first-type data to extract multiple first-type features comprises: performing unsupervised learning on the unlabeled data by using a sparse autoencoder algorithm, to obtain function parameters W and b in the sparse autoencoder algorithm, wherein W is a variable coefficient in the sparse autoencoder algorithm, and wherein b is a constant in the sparse autoencoder algorithm; and performing feature transformation on the labeled data and W and b using a feed-forward autoencoder algorithm to obtain the multiple first-type features. 3. The method according to claim 1, wherein performing the adaptive learning on the second-type data to extract multiple second-type features comprises: performing linear correlation judgment on the second-type data; performing feature extraction according to a result of the linear correlation judgment by using a feature extraction strategy corresponding to the result to obtain a feature transformation matrix; and performing feature transformation on labeled data in the second-type data according to the feature transformation matrix to obtain the multiple second-type features. 4. The method according to claim 3, wherein performing the feature transformation on the labeled data in the second-type data according to the feature transformation matrix to obtain the multiple second-type features comprises: performing feature transformation on the labeled data in the second-type data according to the feature transformation matrix to obtain features obtained from the adaptive learning; and using, when accuracy rates of the features obtained from the adaptive learning meet the preset condition, the features obtained from the adaptive learning as the second-type features. 5. The method according to claim 3, wherein performing the feature transformation on the labeled data in the second-type data according to the feature transformation matrix to obtain the multiple second-type features comprises: performing feature transformation on the labeled data in the second-type data according to the feature transformation matrix to obtain features obtained from the adaptive learning; and performing, when accuracy rates of the features obtained from the adaptive learning do not meet the preset condition, automatic feature selection on the features obtained from the adaptive learning, to determine the second-type features. 6. The method according to claim 3, wherein performing the feature extraction according to the result of the linear correlation judgment using the feature extraction strategy corresponding to the result to obtain the feature transformation matrix comprises: when the result is linear correlation and a proportion of a data volume of the labeled-data set in a second-type data exceeds a preset threshold, performing supervised linear feature extraction by using a linear discriminant analysis (LDA) algorithm, to obtain the feature transformation matrix; when the result is linear correlation and the proportion is lower than the preset threshold, performing non-supervised linear feature extraction by using a principal component analysis (PCA) algorithm, to obtain the feature transformation matrix; when the result is non-linear correlation and the proportion exceeds the preset threshold, performing supervised linear feature extraction using a kernel linear discriminant analysis (KLDA) algorithm to obtain the feature transformation matrix; and when the result is non-linear correlation and the proportion is lower than the preset threshold, performing non-supervised non-linear feature extraction to obtain the feature transformation matrix. 7. The method according to claim 4, wherein performing, when the accuracy rates of the features obtained from the adaptive learning do not meet the preset condition, automatic feature selection on the features obtained from the adaptive learning to determine the second-type features comprises: removing constants from the features obtained from the adaptive learning to obtain multiple primary features; and sorting and screening the multiple primary features according to information gains of the multiple primary features and a degree of similarity between the multiple primary features to obtain the second-type features. 8. A feature set determining apparatus, comprising: a transceiver; a storage configured to store a computer executable instruction; and a processor coupled to the storage and the transceiver and configured to execute the computer executable instruction to cause the feature set determining apparatus to perform operations comprising: obtaining, according to a received feature set determining request, data used for feature learning, wherein the feature set determining request comprises a learning objective of the feature learning; performing type analysis on the data to divide the data into first-type data and second-type data, wherein the first-type data is non-numeric-type data, and wherein the second-type data is numeric-type data; performing semi-supervised learning on the first-type data to extract multiple first-type features; performing adaptive learning on the second-type data to extract multiple second-type features; evaluating the multiple first-type features and the multiple second-type features to obtain an optimal feature set, wherein the optimal feature set comprises multiple features with accuracy rates meeting a preset condition, and wherein the accuracy rate is a degree of closeness between each feature and the learning objective; and outputting the optimal feature set, to respond to the feature set determining request. 9. The feature set determining apparatus according to claim 8, wherein the first-type data comprises labeled data and unlabeled data, and wherein the operation of performing the semi-supervised learning on the first-type data to extract multiple first-type features comprises: performing unsupervised learning on the unlabeled data using a sparse autoencoder algorithm, to obtain function parameters W and b in the sparse autoencoder algorithm, wherein W is a variable coefficient in the sparse autoencoder algorithm, and wherein b is a constant in the sparse autoencoder algorithm; and performing feature transformation on the labeled data and W and b using a feed-forward autoencoder algorithm to obtain the multiple first-type features. 10. The feature set determining apparatus according to claim 8, wherein the operation of performing the adaptive learning on the second-type data to extract multiple second-type features comprises: performing linear correlation judgment on the second-type data; performing feature extraction according to a result of the linear correlation judgment using a feature extraction strategy corresponding to the result to obtain a feature transformation matrix; and performing feature transformation on labeled data in the second-type data according to the feature transformation matrix to obtain the multiple second-type features. 11. The feature set determining apparatus according to claim 10, wherein performing the feature transformation on labeled data in the second-type data according to the feature transformation matrix to obtain the multiple second-type features comprises: performing feature transformation on the labeled data in the second-type data according to the feature transformation matrix to obtain features obtained from the adaptive learning; and using, when accuracy rates of the features obtained from the adaptive learning meet the preset condition, the features obtained from the adaptive learning as the second-type features. 12. The feature set determining apparatus according to claim 10, wherein performing the feature transformation on labeled data in the second-type data according to the feature transformation matrix to obtain the multiple second-type features comprises: performing feature transformation on the labeled data in the second-type data according to the feature transformation matrix to obtain features obtained from the adaptive learning; and performing, when accuracy rates of the features obtained from the adaptive learning do not meet the preset condition, automatic feature selection on the features obtained from the adaptive learning to determine the second-type features. 13. The feature set determining apparatus according to claim 10, wherein the operation of performing the feature extraction according to a result of the linear correlation judgment using a feature extraction strategy corresponding to the result to obtain a feature transformation matrix comprises: when the result is linear correlation and a proportion of a data volume of the labeled-data set in a second-type data exceeds a preset threshold, performing supervised linear feature extraction using a linear discriminant analysis (LDA) algorithm to obtain the feature transformation matrix; when the result is linear correlation and a proportion is lower than the preset threshold, performing non-supervised linear feature extraction using a principal component analysis (PCA) algorithm to obtain the feature transformation matrix; when the result is non-linear correlation and a proportion exceeds the preset threshold, performing supervised linear feature extraction using a kernel linear discriminant analysis (KLDA) algorithm to obtain the feature transformation matrix; and when the result is non-linear correlation and a proportion is lower than the preset threshold, performing non-supervised non-linear feature extraction to obtain the feature transformation matrix. 14. The feature set determining apparatus according to claim 12, wherein the operation of performing, when the accuracy rates of the features obtained from the adaptive learning do not meet the preset condition, automatic feature selection on the features obtained from the adaptive learning to determine the second-type features comprises: removing constants from the features obtained from the adaptive learning to obtain multiple primary features; and sorting and screening the multiple primary features according to information gains of the multiple primary features and a degree of similarity between the multiple primary features to obtain the second-type features. 15. A computer-readable storage medium comprising instructions which, when executed by a computer, cause the computer to perform operations comprising: obtaining, according to a received feature set determining request, data used for feature learning, wherein the feature set determining request comprises a learning objective of the feature learning; performing type analysis on the data to divide the data into first-type data and second-type data, wherein the first-type data is non-numeric-type data, and wherein the second-type data is numeric-type data; performing semi-supervised learning on the first-type data to extract multiple first-type features; performing adaptive learning on the second-type data to extract multiple second-type features; evaluating the multiple first-type features and the multiple second-type features to obtain an optimal feature set, wherein the optimal feature set comprises multiple features with accuracy rates meeting a preset condition, and wherein the accuracy rate is a degree of closeness between each feature and the learning objective; and outputting the optimal feature set to respond to the feature set determining request. 16. The computer-readable storage medium according to claim 15, wherein the first-type data comprises labeled data and unlabeled data, and wherein the operation of performing the semi-supervised learning on the first-type data to extract the multiple first-type features comprises: performing unsupervised learning on the unlabeled data using a sparse autoencoder algorithm to obtain function parameters W and b in the sparse autoencoder algorithm, wherein W is a variable coefficient in the sparse autoencoder algorithm, and wherein b is a constant in the sparse autoencoder algorithm; and performing feature transformation on the labeled data and W and b using a feed-forward autoencoder algorithm to obtain the multiple first-type features. 17. The computer-readable storage medium according to claim 15, wherein the operation of performing adaptive the learning on the second-type data to extract multiple second-type features comprises: performing linear correlation judgment on the second-type data; performing feature extraction according to a result of the linear correlation judgment using a feature extraction strategy corresponding to the result to obtain a feature transformation matrix; and performing feature transformation on labeled data in the second-type data according to the feature transformation matrix to obtain the multiple second-type features. 18. The computer-readable storage medium according to claim 17, wherein the operation of performing the feature transformation on the labeled data in the second-type data according to the feature transformation matrix to obtain the multiple second-type features comprises: performing feature transformation on the labeled data in the second-type data according to the feature transformation matrix to obtain features obtained from the adaptive learning; using, when accuracy rates of the features obtained from the adaptive learning meet the preset condition, the features obtained from the adaptive learning as the second-type features; and performing, when accuracy rates of the features obtained from the adaptive learning do not meet the preset condition, automatic feature selection on the features obtained from the adaptive learning to determine the second-type features. 19. The computer-readable storage medium according to claim 17, wherein the operation of performing the feature extraction according to the result of the linear correlation judgment using the feature extraction strategy corresponding to the result to obtain the feature transformation matrix comprises: when the result is linear correlation and a proportion of a data volume of the labeled-data set in a second-type data exceeds a preset threshold, performing supervised linear feature extraction by using a linear discriminant analysis (LDA) algorithm to obtain the feature transformation matrix; when the result is linear correlation and the a proportion is lower than the preset threshold, performing non-supervised linear feature extraction using a principal component analysis (PCA) algorithm to obtain the feature transformation matrix; when the result is non-linear correlation and the proportion exceeds the preset threshold, performing supervised linear feature extraction using a kernel linear discriminant analysis (KLDA) algorithm to obtain the feature transformation matrix; and when the result is non-linear correlation and the proportion is lower than the preset threshold, performing non-supervised non-linear feature extraction to obtain the feature transformation matrix. 20. The computer-readable storage medium according to claim 18, wherein the operation of performing, when the accuracy rates of the features obtained from the adaptive learning do not meet the preset condition, the automatic feature selection on the features obtained from the adaptive learning to determine the second-type features comprises: removing constants from the features obtained from the adaptive learning to obtain multiple primary features; and sorting and screening the multiple primary features according to information gains of the multiple primary features and a degree of similarity between the multiple primary features to obtain the second-type features.