公开(公告)号：US20230112576A1

公开(公告)日：2023-04-13

申请号：US18053303

申请日：2022-11-07

Applicant: BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD.

Inventor： Haocheng LIU ,  Cai CHEN ,  Bohao FENG ,  Shijie XU ,  Jian TIAN

IPC: G06N3/047

Abstract: Data processing techniques are provided. The techniques include: obtaining a first prediction data set, a model feature list and configuration information, wherein the model feature list indicates a plurality of features required by a data analysis model; generating a second prediction data set based on the model feature list and the first prediction data set, wherein the feature dimension of prediction data in the second prediction data set is smaller than the feature dimension of prediction data in the first prediction data set; performing feature transformation on a feature of the prediction data in the second prediction data set based on the configuration information to generate a third prediction data set; and inputting the third prediction data set into the data analysis model to obtain a prediction result.

公开(公告)号：US20230145408A1

公开(公告)日：2023-05-11

申请号：US18148177

申请日：2022-12-29

Applicant: Beijing Baidu Netcom Science Technology Co., Ltd.

Inventor： Haocheng LIU ,  Jingyu XU ,  Cai CHEN ,  Shuo LI

IPC: G06F16/26

CPC classification number: G06F16/26

Abstract: A method of processing a feature information is provided, which relates to a field of data processing, in particular to fields of artificial intelligence and big data. The method includes: determining at least one candidate division point in a value range of the feature information, and determining an information value corresponding to each candidate division point; determining a target division point based on the information value; dividing the value range based on the target division point, so as to obtain two sub-ranges; determining a sub-range meeting a termination condition in the two sub-ranges as a target interval, determining a sub-range not meeting the termination condition in the two sub-ranges as a new value range, and returning to perform the step of determining at least one candidate division point in a value range until both sub-ranges meet the termination condition, so as to obtain a plurality of target intervals.

公开(公告)号：US20230222579A1

公开(公告)日：2023-07-13

申请号：US17972896

申请日：2022-10-25

Applicant: Beijing Baidu Netcom Science Technology Co., Ltd.

Inventor： Haocheng LIU ,  Cai CHEN

IPC: G06Q40/02

CPC classification number: G06Q40/025

Abstract: The present disclosure provides a method and apparatus for iterating a credit scorecard model, electronic device, and storage medium, and relates to the technical field of machine learning, in particular, to the field of smart finance and risk control technologies. A method can include: dividing multiple to-be-classified samples into a first part sample and a second part sample; classifying the first part sample by using a first credit scorecard model to obtain a first prediction result, and classifying the second part sample by using the first credit scorecard model to obtain a second prediction result; and determining an input sample of a second credit scorecard model based on the first prediction result and the second prediction result, and iterating the first credit scorecard model according to the second prediction result and multiple history samples, to obtain a target credit scorecard model.

公开(公告)号：US20230186613A1

公开(公告)日：2023-06-15

申请号：US17967790

申请日：2022-10-17

Applicant: Beijing Baidu Netcom Science Technology Co., Ltd.

Inventor： Haocheng LIU

IPC: G06V10/80 ,  G06V10/762

CPC classification number: G06V10/817 ,  G06V10/762

Abstract: The present disclosure provides a sample classification method and apparatus, an electronic device and a storage medium, and relate to the technical field of data mining, in particular to the field of machine learning. The method includes that: a sample to be classified is acquired, and a sample feature dimension of the sample to be classified is greater than a preset threshold; feature encoding is performed on a sample feature of the sample to be classified according to various feature encoding modes to obtain multiple feature vectors; and clustering analysis is performed on the multiple feature vectors to determine a target class of the sample to be classified.