公开(公告)号：US11798018B2

公开(公告)日：2023-10-24

申请号：US15062937

申请日：2016-03-07

Applicant: ADOBE INC.

Inventor： Wei Zhang ,  Shiladitya Bose ,  Said Kobeissi ,  Scott Allen Tomko ,  Jeremy W King

IPC: G06N20/00 ,  G06Q30/0204 ,  G06Q10/0639 ,  G06Q50/00 ,  G06F16/9535 ,  G06F16/35 ,  G06N3/08 ,  G06N3/044 ,  G06N3/045

CPC classification number: G06Q30/0205 ,  G06F16/35 ,  G06F16/9535 ,  G06Q10/06395 ,  G06Q50/01 ,  G06N3/044 ,  G06N3/045 ,  G06N3/08

Abstract: Systems and methods provide for feature selection that combines semantic classification and generative filtering with forward selection. Features from an original feature set are divided into feature subsets corresponding to ranked semantic classes. Additionally, low quality features are removed from consideration. Features are selected for a reduced feature set by iteratively processing the feature subsets using forward selection in an order corresponding to the ranking of the semantic classes. The reduced feature set is used to generate a predictive model.

公开(公告)号：US10346861B2

公开(公告)日：2019-07-09

申请号：US14933254

申请日：2015-11-05

Applicant: ADOBE INC.

Inventor： Wei Zhang ,  Said Kobeissi ,  Anandhavelu Natarajan ,  Shiv Kumar Saini ,  Ritwik Sinha ,  Scott Allen Tomko

IPC: G06N3/08 ,  G06Q30/02

Abstract: Embodiments of the present invention relate to providing business customers with predictive capabilities, such as identifying valuable customers or estimating the likelihood that a product will be purchased. An adaptive sampling scheme is utilized, which helps generate sample data points from large scale data that is imbalanced (for example, digital website traffic with hundreds of millions of visitors but only a small portion of them are of interest). In embodiments, a stream of sample data points is received. Positive samples are added to a positive list until the desired number of positives is reached and negative samples are added to a negative list until the desired number of negative samples is reached. The positive list and the negative list can then be combined, shuffled, and fed into a prediction model.