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公开(公告)号：US20200293554A1
公开(公告)日：2020-09-17
申请号：US16888575
申请日：2020-05-29
发明人： Yalin Zhang , Longfei Li
摘要： Implementations of the present specification provide abnormal sample prediction methods and apparatuses. The method includes: obtaining a sample to be tested, wherein the sample to be tested comprises feature data with a given dimension, and wherein the given dimension is a first quantity; performing dimension reduction processing on the sample to be tested by using multiple dimension reduction methods to obtain multiple processed samples; inputting the multiple processed samples to multiple corresponding processing models to obtain scores of the multiple processed samples, wherein an ith processing model Mi in the multiple processing models scores the corresponding processed sample Si based on a hypersphere Qi; determining a comprehensive score of the sample to be tested based on scores of the multiple processed samples; and classifying, based on the comprehensive score, the sample to be tested as an abnormal sample.
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公开(公告)号：US10592783B2
公开(公告)日：2020-03-17
申请号：US16366794
申请日：2019-03-27
发明人： Wenhao Zheng , Yalin Zhang , Longfei Li
摘要： A feature extraction is performed on transaction data to obtain a user classification feature and a transaction classification feature. A first dimension feature is constructed based on the user classification feature and the transaction classification feature. A dimension reduction processing is performed on the first dimension feature to obtain a second dimension feature. A probability that the transaction data relates to a risky transaction is determined based on a decision classification of the second dimension feature, where the decision classification is based on a pre-trained deep forest network including a plurality of levels of decision tree forest sets.
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公开(公告)号：US20190303943A1
公开(公告)日：2019-10-03
申请号：US16366841
申请日：2019-03-27
发明人： Yalin Zhang , Wenhao Zheng , Longfei Li
摘要： The present disclosure describes techniques for object classification using deep forest networks. One example method includes classifying a user object including features associated with the user based on a deep forest network including identifying one or more user static features, one or more user dynamic features, and one or more user association features from the features included in the user object; providing the user static features to first layers, the user dynamic features to second layers, and the user association features to third layers, the first, second, and third layers being different and each providing classification data to the next layer based at least in part on the input data and the provided user features.
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公开(公告)号：US20190042763A1
公开(公告)日：2019-02-07
申请号：US16053606
申请日：2018-08-02
发明人： Peilin Zhao , Jun Zhou , Xiaolong Li , Longfei Li
摘要： Techniques for data sharing between a data miner and a data provider are provided. A set of public parameters is downloaded from the data miner. The public parameters are data miner parameters associated with a feature set of training sample data. A set of private parameters in the data provider can be replaced with the set of public parameters. The private parameters are data provider parameters associated with the feature set of training sample data. The private parameters are updated to provide a set of update results. The private parameters are updated based on a model parameter update algorithm associated with the data provider. The update results is uploaded to the data miner.
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公开(公告)号：US10785241B2
公开(公告)日：2020-09-22
申请号：US16802147
申请日：2020-02-26
发明人： Longfei Li
摘要： Features of multiple dimensions are extracted from information included in a URL access request. A risk score of the URL access request is obtained by providing the features to a predetermined URL attack detection model for prediction calculation, where the predetermined URL attack detection model is a machine learning model obtained through training based on the Isolation Forest machine learning algorithm. It is determined, based on the risk score, that the URL access request is a URL attack request.
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公开(公告)号：US20190303728A1
公开(公告)日：2019-10-03
申请号：US16366794
申请日：2019-03-27
发明人： Wenhao Zheng , Yalin Zhang , Longfei Li
摘要： A feature extraction is performed on transaction data to obtain a user classification feature and a transaction classification feature. A first dimension feature is constructed based on the user classification feature and the transaction classification feature. A dimension reduction processing is performed on the first dimension feature to obtain a second dimension feature. A probability that the transaction data relates to a risky transaction is determined based on a decision classification of the second dimension feature, where the decision classification is based on a pre-trained deep forest network including a plurality of levels of decision tree forest sets.
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公开(公告)号：US20190005586A1
公开(公告)日：2019-01-03
申请号：US16022194
申请日：2018-06-28
发明人： Yuxiang Lei , Guanru Li , Wei Ding , Jing Huang , Chunping Tan , Shiyi Chen , Mingqian Shi , Peilin Zhao , Longfei Li , Zhiqiang Zhang
摘要： A plurality of variable data of personal attribute information associated with at least one vehicle insurance user is received at a prediction server. Based on a service scenario requirement, a pre-constructed prediction algorithm is selected. The plurality of variable data is processed by one or more processors using the pre-constructed prediction algorithm. At least one prediction result is generated as the prediction server.
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8.发明申请公开(公告)号：US20190287114A1
公开(公告)日：2019-09-19
申请号：US16355439
申请日：2019-03-15
发明人： Longfei Li
摘要： Techniques for identifying fraudulent transactions are described. In one example method, an operation sequence and time difference information associated with a transaction are identified by a server. A probability that the transaction is a fraudulent transaction is predicted based on a result provided by a deep learning network, where the deep learning network is trained to predict fraudulent transactions based on operation sequences and time differences associated with a plurality of transaction samples, and where the deep learning network provides the result in response to input including the operation sequence and the time difference information associated with the transaction.
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公开(公告)号：US20190236114A1
公开(公告)日：2019-08-01
申请号：US16257741
申请日：2019-01-25
发明人： Longfei Li
CPC分类号： G06F17/18 , G06F16/215 , G06K9/6262 , G06K9/6284
摘要： This specification describes techniques for detecting abnormal data in a data set. One example method includes obtaining, by a data processing platform, a to-be-validated data group including to-be-validated data corresponding to a predetermined feature; obtaining, by the data processing platform, a comparison data group including historical data associated with the to-be-validated data group, wherein the historical and the to-be-validated data are from a same data source; performing, by the data processing platform, a two-group significance test on the to-be-validated data group and the comparison data group to generate a test result; and determining, by the data processing platform, whether there is abnormal data in the to-be-validated data group based on the test result.
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公开(公告)号：US20200380524A1
公开(公告)日：2020-12-03
申请号：US16803150
申请日：2020-02-27
发明人： Longfei Li
IPC分类号： G06Q20/40 , G06F17/18 , G06F16/906 , G06F16/9038
摘要： The present specification discloses a method and an apparatus for training a transaction feature generation model, and a method and an apparatus for generating a transaction feature. The method for generating a transaction feature can include the following: obtaining a target dataset, where the target dataset includes some pieces of transaction data; obtaining some original features of the transaction data and determining one or more combination methods for the original features; determining a feature vector of a new feature that is obtained by combining the original features based on each combination method; inputting the feature vector into a trained transaction feature generation model, and outputting a prediction result of the new feature; and selecting some new features whose prediction results meet a specified condition as transaction features generated for the target dataset.
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