WO2023082969A1 - Procédé et système de tarification de combinaison de caractéristiques de données basés sur une valeur de shapley et dispositif électronique - Google Patents

Procédé et système de tarification de combinaison de caractéristiques de données basés sur une valeur de shapley et dispositif électronique Download PDF

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WO2023082969A1
WO2023082969A1 PCT/CN2022/126712 CN2022126712W WO2023082969A1 WO 2023082969 A1 WO2023082969 A1 WO 2023082969A1 CN 2022126712 W CN2022126712 W CN 2022126712W WO 2023082969 A1 WO2023082969 A1 WO 2023082969A1
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data
feature
shapley
buyer
features
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Chinese (zh)
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余海燕
刘珂
缪红霞
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重庆邮电大学
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0283—Price estimation or determination
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/08—Auctions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/03—Credit; Loans; Processing thereof
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/04—Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange

Definitions

  • the present invention relates to machine learning, in particular to a data feature combination pricing method, system and electronic equipment based on Shapley values.
  • a bank uses financial technology to analyze various data, machine learning and forecasting through the purchased feature data, which provides an important tool for solving the problem of information asymmetry.
  • the bank will not only use the data about the enterprise within the banking system, but also use the valuable external data that can be obtained about the operating ability of the enterprise.
  • By capturing the trajectory of the enterprise's production and operation it provides financial institutions with reliable "credit data", which not only improves the possibility of successful loans, but also reduces transaction costs and credit service thresholds.
  • This data transaction process is realized through a third-party data transaction platform, which can not only guarantee the privacy and security of the buyer's data to a certain extent, but also ensure that the price of the data buyer is reasonable through dynamic market pricing.
  • the third-party trading platform needs to price the purchased data in the market, and provide the data and payment fees required by both parties to the transaction.
  • companies that successfully purchase data need to sign a confidentiality agreement with the platform. The data is limited to the company's own business use and cannot be disseminated or re-sold.
  • the third-party data trading platform builds a data feature selection model and a feature value distribution algorithm that approximates the Shapley value, and can judge which feature variables have the greatest impact on the results and which feature variables have less impact on the results based on the obtained results.
  • the buyer pays attention to the set of features with greater influence, and controls risks and reduces losses through machine learning results to a certain extent.
  • the purchase of this data can obtain specific information of the corresponding industry, which provides support for the loan evaluation and analysis of the industry, and can also reduce loan risks.
  • data sellers can also get a profit.
  • the third-party trading platform provides data dynamic pricing methods and systems.
  • a feature selection algorithm based on increasing prediction accuracy is used.
  • the combination of recursive feature elimination method, cross-validation and feature combination can effectively select the data features, and then carry out information mining analysis on the selected data features.
  • the present invention proposes a data feature contribution distribution method based on the Shapley value, which can calculate the corresponding effect of each feature (marginal contribution to prediction accuracy).
  • the monitoring feature data of the transaction is used to realize dynamic pricing by means of auction and multiplication weight update algorithm.
  • the improved multiplicative weight update algorithm realizes dynamic pricing of data characteristics, which is conducive to fully realizing the value of data and bringing additional income to enterprises.
  • the present invention proposes a Data feature combination pricing method, system and electronic equipment based on Shapley value, the method includes being
  • the data buyer and seller construct a transaction, and obtain the predicted value of the current data through the constructed learning model as the payment price of the data.
  • the process of selecting the optimal feature classification variables from the feature classification variables includes the following steps:
  • the estimation of the Shapley value of the feature variable based on the ghost data instance includes randomly selecting an instance from the feature variable, constructing an instance with a certain feature and an instance without the aforementioned feature, and combining the two instances As a ghost data instance.
  • the process of pricing the characteristic variable includes the following steps:
  • the data seller Before trading with the data buyer, the data seller first sets the price p n of the transaction data, the number of buyers and the buyer's quotation, and calculates the data buyer's income function;
  • the seller updates the data price based on the multiplication weight update algorithm, returns to S41, and starts the next round of pricing.
  • G(b n ,p n ) is the buyer's profit function when the seller sets the price of transaction data as p n and the buyer's quotation is b n .
  • the seller’s income function is determined according to the price of the transaction data set by the seller and the quotation of the buyer.
  • the seller’s price is fixed, when the quotation b n is smaller than the price p n of the transaction data set by the seller, as the quotation b n increases The profit of the big buyer increases until the quotation b n is equal to the price p n of the transaction data set by the seller to reach the maximum profit; when the quotation b n is greater than the price p n of the transaction data set by the seller, the buyer’s utility remains at the maximum value and the buyer pays Fees also remain at the same maximum value.
  • the selling price Sn of each sample is:
  • S is the selling price when there is only one piece of data
  • e is the penalty factor
  • the present invention proposes a data feature combination pricing system based on the Shapley value, including a feature selection subsystem and a pricing subsystem.
  • the feature selection subsystem screens features, and the pricing subsystem performs pricing auctions on the screened features;
  • the feature subsystem includes the machine learning model and the Shapley analysis model.
  • the machine learning model performs training and prediction based on the data, sorts the predicted values as the importance of the features, and sends the K features with the greatest importance to the Shapley analysis
  • the model is analyzed; the Shapley analysis model calculates the editorial contribution and the average Shapley value of the feature variable;
  • the present invention also proposes a pricing electronic device based on the combination of data features of the Shapley value, including a processor and a memory, any one of the aforementioned pricing methods based on the combination of data features of the Shapley value according to claim 1, and processing
  • the processor is capable of running a Shapley value-based data feature combination pricing method stored in memory.
  • the designed data transaction model and real-time dynamic pricing algorithm can maximize the long-term profit of the enterprise; at the same time, the characteristic data obtained from the auction also provides data buyers such as banks or insurance companies with loan evaluation business decision support, reducing the loss of loans and compensation .
  • the auction data obtained by the third-party trading platform can be visualized through the transaction control panel to quickly extract key information.
  • Figure 1 is a schematic diagram of the overall architecture of the dynamic pricing based on the combination of Sharpe value data features disclosed in the embodiment of the present invention
  • Fig. 2 is a schematic diagram of data feature selection and sorting based on Sharpley value disclosed in the embodiment of the present invention
  • Fig. 3 is a schematic diagram of a characteristic Shapley value based on machine learning disclosed in an embodiment of the present invention.
  • Fig. 4 is a schematic diagram of auction pricing based on data characteristics disclosed in the embodiment of the present invention.
  • Fig. 5 is a schematic diagram of a data dynamic pricing control panel disclosed in an embodiment of the present invention.
  • Fig. 6 is a schematic diagram of information interaction of a Shapley value-based data feature combination dynamic pricing method disclosed in an embodiment of the present invention
  • Fig. 7 is a schematic diagram of introducing a penalty function based on the Shapley value based on data replicability disclosed by the embodiment of the present invention.
  • Fig. 8 is a schematic structural diagram of a Shapley value data feature combination dynamic pricing method device disclosed in an embodiment of the present invention.
  • Fig. 9 is a schematic structural diagram of another Shapley value data feature combination dynamic pricing method device disclosed in an embodiment of the present invention.
  • the present invention proposes a data feature combination pricing method based on the Shapley value, which specifically includes the following steps:
  • the quality inspector judges whether the data can be traded, and if it can be traded.
  • the quality inspector first deletes the missing data, first deletes the columns (attributes) with a missing rate higher than 10%, and then deletes the rows (tuples) with missing data;
  • the labeled data undergoes multiple rounds of manual inspection;
  • the quality inspector conducts a round of sampling inspection on the marked data, and conducts random sampling or stratified sampling on 50% of the marked data for inspection. If all the marked data are qualified in the first round, then in the second round In the first round of inspection, 25% of the labeled data will be inspected for quality;
  • the quality inspector needs to conduct a full sample inspection of the labeler's data labeling in the second round of inspection;
  • the amount of labeled data inspected in the second round of sampling inspection will be doubled compared with the first round;
  • the amount of labeled data inspected in the second round of sampling inspection will increase by 30% compared with the first round;
  • a learning model based on machine learning is constructed to predict a single instance.
  • the prediction process is "payment”, and "revenue” is the actual prediction of the instance minus the average predicted value of all samples, and the Shapley value of the feature It is the average marginal contribution of the feature in all feature sequences, so as to fairly divide the contribution of each feature to the prediction result.
  • the characteristic Shapley value estimation of construction what solve is the characteristic value distribution (contribution) problem based on Shapley value, and specific content is to use the difference of the average prediction value of the prediction result of specific instance and data set as the characteristic of this instance Feature Shapley value (revenue), through two random examples to simulate the appearance or absence of features, calculate the marginal value of features in a specific instance, and use the mean of the absolute value of its Shapley value as the feature in the data set global value.
  • the data features in the experimental dataset work together in a machine learning algorithm to produce a predicted value.
  • Shapley value to allocate the value of each feature according to its marginal value (contribution), quantify the impact of different input features on the output prediction results of the training model, and the distribution of feature values balances the data prediction accuracy and prediction cost, and determines the choice of a certain feature.
  • This embodiment proposes a dynamic pricing method based on the combination of data features of the Shapley value.
  • the schematic diagram of the general architecture is shown in Figure 1, which includes the following steps:
  • the sensor data of the third-party data trading platform is associated with relevant historical files to obtain a characteristic data set.
  • Sensor data comes from data sets collected by data sellers using sensors in all aspects of production and operation.
  • the number of remaining features reaches the required number of features, and features are selected by recursively reducing the size of the feature set under investigation.
  • Use the cross-validation method to finally determine the k features with increasing prediction accuracy, and then combine and arrange the k features generated by the above-mentioned CV-RFE and the incremental screening of prediction accuracy to output all subsets, resulting in 2 k -1 (remove the empty set) feature subsets, then set all feature subsets as training set and verification set, bring them into model training and calculate the accuracy of feature subsets respectively, take the mean value of multiple rounds of iterative experiments, and finally compare the feature subsets with the corresponding Accuracy output.
  • Data dynamic pricing system and data transaction control panel based on multiplication weight update algorithm. Based on the idea of multiplicative weights and the characteristics of data transactions, a pricing algorithm based on multiplicative weight update weights is designed to maximize the long-term income of the platform, so that the generated price income is the same as the income obtained by the optimal price in hindsight.
  • the average regret value of participants is 0, which is conducive to the maximum utility of both buyers and sellers, forming a benign transaction relationship, and giving full play to the value of data; the data transaction control panel summarizes the obtained auction price and other information, and displays it in a variety of visual information such as graphics.
  • An important feature of the present invention is the feature selection and feature Shapley value estimation algorithm based on machine learning to obtain the distribution of individual feature prediction contributions, as well as the correlation trend and global importance of features. This embodiment further illustrates this.
  • the feature data (201) is collected by sensors and the like and then divided into a training set (202) and a verification set (209), and the training set is divided into optimal feature selection under a fixed number of features , using CV-RFE feature selection (203) to obtain the optimal number of features (204); the optimal feature selection under the variable number of features is to perform feature combination arrangement (205), and determine the optimal combination of the number of features (206) .
  • the optimal combination of the number of features and the number of features are performed by sensors and the like and then divided into a training set (202) and a verification set (209), and the training set is divided into optimal feature selection under a fixed number of features , using CV-RFE feature selection (203) to obtain the optimal number of features (204); the optimal feature selection under the variable number of features is to perform feature combination arrangement (205), and determine the optimal combination
  • the sorted feature vectors are used in the prediction model of machine learning (301) to obtain the prediction results (302), and then all the feature data and prediction results are brought into the Shapley value analysis model (303 ). Finally, the global importance of features, the trend of correlation between features and prediction results, and the distribution of prediction contributions of individual feature data are obtained.
  • the contribution analysis of prediction results using the Shapley value method can be divided into two levels. On the global level (306, 305), the distribution of the Shapley value can be used to describe the specific influence, law and correlation of features. ; At the local level (304), the quantified contribution of each feature in each sample prediction can be given. After using the Shapley value algorithm to get the value contribution of each feature, it can be balanced with the cost of data collection.
  • Fig. 4 is a data feature auction transaction pricing mechanism of a multiplication weight update algorithm disclosed in an embodiment of the present invention.
  • the multiplication weight update process maintains the weight of each pricing strategy and randomly selects strategies for repeated iterations to achieve the maximum long-term operating income.
  • a decision set contains ⁇ alternative decisions, corresponding to a specific income ⁇ (income is not a priori), and multiple rounds of selection are performed on it.
  • the current weight of each decision is multiplied by the income factor related to the current round of income and updated Weight, the decision-making party repeatedly makes choices and obtains corresponding benefits. After multiple rounds, the weight value of the strategy with the highest profit will become prominent, and the probability of the strategy being selected will increase significantly.
  • the core idea of the multiplication weight update algorithm is illustrated. Assuming that the auction price trend is random, and it is desired to predict the state of the auction price (fall or rise) through the opinions of experts, all N experts form a set C. Before the data auction, the suggestion of an expert i in C is randomly selected to predict the trend of the data auction (down or up). If the expert’s prediction is wrong, the price will be 1; if the prediction is correct, the loss will be 0. Since expert i is randomly selected for prediction, in order to make better decisions, the algorithm aims to control the prediction near the best-performing expert in the long run, that is, in the next round of prediction, the probability of being selected by the expert who made the correct prediction is higher.
  • each round obeys the opinions of the weighted majority of experts.
  • the initial weight of N experts be 1
  • each round of forecast results is two (down or up) to choose one; introduce the parameter ⁇ ( ⁇ 0.5) as a factor related to income, and in the next round of selection, give the prediction error expert (1 - ⁇ ) times lowering penalty.
  • the upper bound of the error of the algorithm is The multiplication weight update algorithm mainly has the following four steps:
  • Step 1 The data seller sets the current price of the data as p n ; the number of data buyers is n, and the data is purchased sequentially; the data buyer n quotes the data to be purchased as b n , and for any group of n ⁇ [N] buyers
  • the bids b n all come from a closed and bounded set B, the diameter of the set B is D, and D ⁇ , that is, b n ⁇ B.
  • Step 2 The income function of the data buyer is G(p n , b n ), which is related to the buyer’s quotation b n and the existing price p n . Different quotations and different current prices will lead to different income for buyer n .
  • Step 3 The data seller determines the buyer’s payment function RF(p n , b n ) based on the existing price p n and the buyer’s quotation b n , which is the Lipschitz function and is used to calculate the buyer’s final payment .
  • L is the Lipschitz coefficient
  • b is the buyer's quotation
  • p (1) and p (2) are two prices.
  • Step 4 The data buyer pays the fee R n , takes away the data prediction result, and completes a single transaction; the data seller updates the data price p n+1 , returns to the first step, and starts the next round of pricing.
  • B max ⁇ R is the buyer’s maximum offer of set B
  • B net ( ⁇ ) ⁇ R is the minimum ⁇ grid of B, which means For all x ⁇ B, there is x 0 ⁇ K such that
  • the elements in B net ( ⁇ ) are different prices tested in the multiplicative weighting algorithm, and N refers to the number of different prices.
  • the present invention also describes a data dynamic pricing control panel disclosed in the embodiment (see FIG. 5 ).
  • the relevant introduction (501) of the auction data input by the third-party data trading platform such as the industry information and attributes of the data, for data buyers to view.
  • Multiple data buyers enter the auction market anonymously and choose whether to conduct an auction (502) based on data-related information. If they choose an auction, they will conduct a buyer's bidding (503). If they do not choose an auction, they will wait for the next round of data auction. If only one data buyer bids, the data will belong to that buyer; if multiple buyers bid, the auction will be conducted according to the principle of "the highest bidder wins”.
  • the third-party data trading platform summarizes the transaction records (505) based on the transaction volume obtained in the above auction steps, such as transaction volume, transaction amount year-on-year, buyer industry proportion display and other information, and displays them in various visual forms such as graphics , and supplemented by relevant information research and judgment and other decision-making.
  • This embodiment provides an information interaction process of a Shapley value data feature combination pricing method, as shown in Figure 6, which is described from the perspective of the data buyer, the third-party data transaction platform server, and the seller's control terminal panel, including the following steps :
  • the third-party data trading platform transmits and acquires various feature data on site in the production and operation of the data seller (601), and then performs CV-RFE feature selection and sorting (603) on the acquired feature data to obtain a feature data combination and sort;
  • the third-party data transaction platform conducts dynamic transaction pricing on the auction (605).
  • the data seller determines whether to purchase the data according to the value of the auction data and whether it can increase the revenue of the enterprise.
  • the third-party data trading platform collects auction prices, and judges abnormal auction prices such as being too low or too high (606);
  • the payment distribution function of A is R n (A); the output R n (A) of the sticky Shapley value algorithm (Table 3) is ⁇ - Replication robustness gains.
  • the datasets on the market have no replica, 1 replica and 2 replicas.
  • the overall income distribution is S n ; when there is 1 copy in the market (702), there are a total of 2 data sets in the market, and the penalty function e is introduced, then the income of each data set Each distribution is 1/2Se; when there are 2 replicas in the market (703), there are 3 data sets in the market, and the penalty function becomes e 2 , then the income distribution of each data set is 1/3Se 2 , as And so on.
  • the pricing S is determined each time, when the same data is sold to multiple users, the data is priced according to the data copy price. If the data is copied into i samples, the selling price S n of each sample is:
  • S is the selling price when there is only one piece of data, which is different from the quotation b n and the price p n of the data set by the seller.
  • S is the actual selling price of the selling price when there is only one piece of data; e is the penalty factor.
  • the data testing device may be electronic equipment.
  • the data testing device may include: the processor 801 transmits effective information, the memory 802 is responsible for storing data such as characteristic data, and after performing characteristic selection and sorting of the production and operation data of the enterprise (803), the data analyzed by the Shapley value is used for auction and the result It is transmitted to the control panel terminal, the communication interface 803 refers to the interface between the central processing unit and the standard communication subsystem, and the control panel 804 performs screen display.
  • FIG. 9 is a schematic structural diagram of another Shapley value data feature combination dynamic pricing method device disclosed in an embodiment of the present invention.
  • the pricing device may be an electronic device.
  • Three algorithms among the present invention carry out according to the following steps:
  • the device can use the data obtained by cascading sensors and files to pass through the acquisition unit 901 and send it to the calculation unit for analysis.
  • the calculation unit 902 receives the signal, it predicts and sorts the feature data through the control unit 903 and so on, and then the storage unit 904 stores the predicted and sorted feature data, and the storage unit transmits the result to the output unit 905 after the work is completed.
  • 3Dynamic pricing of data based on the multiplication weight update algorithm input the existing price, number of buyers and quotations set by the seller to the acquisition unit 901, and calculate the buyer’s payment fee through the calculation unit 902 and control unit 903 according to the buyer’s income function and payment function,
  • the storage unit 804 stores the relevant data, and outputs the data price updated by the next round of sellers.

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Abstract

La présente invention concerne l'apprentissage automatique, et en particulier un procédé et un système de tarification de combinaison de caractéristiques de données sur la base d'une valeur de Shapley et un dispositif électronique. Le procédé consiste : à collecter des variables de caractéristiques d'un ensemble de données de caractéristiques fournies par un vendeur, et à prétraiter les variables de caractéristiques ; à construire un modèle d'apprentissage sur la base d'un apprentissage automatique, et à sélectionner une variable de classification de caractéristiques optimale à partir de variables de classification de caractéristiques ; à estimer des valeurs de Shapley de caractéristiques construites sur la base d'instances de données fantômes de façon à calculer une contribution marginale et une valeur de Shapley moyenne de variables de caractéristiques sélectionnées ; et en fonction de la contribution marginale et de la valeur de Shapley moyenne des variables de caractéristiques, à déterminer si les variables de caractéristiques peuvent être soumises à une transaction, et si tel est le cas, à effectuer la transaction. Dans les modes de réalisation de la présente invention, une maximisation des avantages à long terme d'un fournisseur de données peut être réalisée, une évaluation de risque du vendeur de données sur la société d'acheteur de données est satisfaite, et la perte de risque est réduite.
PCT/CN2022/126712 2021-11-11 2022-10-21 Procédé et système de tarification de combinaison de caractéristiques de données basés sur une valeur de shapley et dispositif électronique WO2023082969A1 (fr)

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