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42 results about "Concept drifting" patented technology

Data stream anomaly detection system based on empirical features and convolution neural network

The invention discloses a data stream anomaly detection system based on empirical characteristics and convolution neural network. The system includes an empirical feature extraction module, which is used to identify statistical features and header features as features based on artificial experience, which play a more important role in data packet anomaly recognition; a bit stream conversion picture module used to convert the data stream into the form of two-dimensional gray-scale picture, and then through the convolutional neural network perception, the global high-level perception features are extracted; a fusion splicing module used for fusing the above modules as the data stream characteristics and identifying abnormal data streams by using the full connection layer of the neural network; a distillation model module that replaces complex networks in actual deployment; a concept drift fine-tuning module updates the detection model of concept drift; an update experience database module adding new network attacks or hidden attack instructions to the artificial experience database. The invention accurately and efficiently detects abnormal behaviors such as network failure, user misoperation, network attack and the like.
Owner:ARMY ENG UNIV OF PLA

User score-based project recommendation method

InactiveCN105740444AGood sparsity resistanceAddressing Concept DriftSpecial data processing applicationsPersonalizationTime factor
The invention discloses a user score-based project recommendation method. The method comprises the following steps: in allusion to the dynamism and diversity of interests of a user in a recommendation system, effectively fusing a maintenance dose function on the basis of the sores of user projects and completing the global learning of potential interests of the user by adopting a probability topic model according to the global influences, on the interests of the user, of the time factors; and in allusion to the sensitivity, for potential scenario change, of the learning process, user personalization-oriented secondary updating learning is carried out on the interests on the basis of a concept drift problem according to the local influences, on the potential interests of the user, of the time factors; and finally calculating the degrees of supporting the projects by the user through analyzing the interests of the user, and carrying out sorting to generate a project recommendation list. According to the method, the influences, caused by the recommendation performance, of the concept drift problem can be effectively avoided and the whole recommendation quality of the system can be improved under the condition of sufficiently mining the potential interests of the user.
Owner:GUILIN UNIV OF ELECTRONIC TECH

Concept drift detection method and system based on weighted sampling and electronic equipment

ActiveCN113033643ADetection drift degreeAddressing Concept DriftCharacter and pattern recognitionMachine learningReal-time dataEngineering
The invention provides a concept drift detection method and system based on weighted sampling and electronic equipment, and the method comprises the steps: training an offline model based on historical data, carrying out the model reasoning of online data through employing the offline model, and outputting a model reasoning result; receiving the online real-time data, calculating a concept drift value based on the online real-time data and the historical data, judging whether the concept drift value is greater than a drift threshold, if so, confirming that the online real-time data has concept drift, and if not, confirming that the online real-time data does not have concept drift; when the concept drift exists in the online real-time data, updating the offline model and training data for training the offline model based on the online real-time data and the historical data; and performing model reasoning on the online data based on the updated offline model, and outputting a model reasoning result. According to the method, the drift degree of the current model can be effectively detected, the drift degree serves as a basis for model retraining / updating, and the concept drift problem of the AI model is intelligently solved.
Owner:SHANGHAI JIAO TONG UNIV

Adaptive network flow concept drift detection method based on information entropy

The invention discloses an adaptive network flow concept drift detection method based on information entropy, and relates to the technical field of data processing. The concept drift problem caused byflow characteristic changes due to time lapse and different network environments is solved. By using sliding window techniques, aiming at new and old data streams, discretizing the flow characteristic attributes into a plurality of branches; comparing the plurality of flow characteristics together; comparing differences of new and old data stream sliding windows by counting information entropiesof each characteristic attribute and each branch; and when specific detection is carried out, the method comprises the following steps: firstly, solving a threshold calculation formula of the size ofa sliding window by utilizing a Hoeffding boundary theory; then, determining the size of a sliding window by utilizing a Tie-buffering method; according to the method, firstly, obtaining a new data flow sliding window and an old data flow sliding window, then calculating entropy values of the new data flow sliding window and the old data flow sliding window through information entropy, finally, jusging whether concept drifting happens to data or not according to threshold values and information entropy difference values of the new window and the old window, thus concept drifting can be effectively detected, classifiers are updated, and good classification performance and generalization capacity are shown.
Owner:INSPUR ARTIFICIAL INTELLIGENCE RES INST CO LTD SHANDONG CHINA

Aluminum profile extrusion process flow data anomaly detection method and device based on isolated forest algorithm and storage medium

ActiveCN111931834ASolve the problem of delayed result feedbackSolve the problem of imprecise anomaly detection resultsCharacter and pattern recognitionManufacturing computing systemsStreaming dataAlgorithm
The invention relates to the technical field of streaming data anomaly detection and more specifically relates to an aluminum profile extrusion process stream data anomaly detection method and devicebased on an isolated forest algorithm and a storage medium. The method comprises the following steps: S10, reading original stream data of an extrusion process of an extruder, and initializing a multi-feature semi-space isolated forest model through the original stream data; S20, entering a detection period, and using the multi-feature semi-space isolation forest model to perform anomaly detectionon the stream data in the current period; S30, judging whether the detection period is finished or not; if not, returning to the step S20, updating the detection period, and if so, entering the nextstep; S40, judging whether the abnormal rate of the current period is greater than a threshold value or not, if so, indicating that concept drift exists, updating the model by using the data of the current period; if not, returning to the step S20, and entering the next period for detection until all periods are detected. The model can be updated in real time, and the problem that abnormal detection results are inaccurate due to noise and concept drift existing in streaming data is solved.
Owner:GUANGDONG UNIV OF TECH

Multi-label data flow classification method based on incremental learning

The invention discloses a multi-label data stream classification method based on incremental learning, and the method comprises the steps: step 1, an initial training stage: carrying out the modelingof a multi-label data stream into data blocks with a fixed instance number, carrying out the naive Bayes model training of each data block according to the initial data block, and obtaining a clustercenter set through employing a KMeans algorithm, wherein the trained naive Bayes classification model and the cluster center set jointly serve as a base classifier; step 2, a concept drift detection stage: when the number of the base classifiers in the naive Bayes integration model reaches a certain number in the initial learning stage, carrying out concept drift detection from the data level andthe model level respectively; step 3, an increment updating stage: when a latest data block Dt comes, updating the base classifier by using information carried by each sample in the Dt for each base classifier in the integration model, and performing instance information updating. The concept drift in the data flow can be detected in time, the situation that the algorithm performance encounters large downslide when the concept drift occurs is avoided, the latest data can be subjected to incremental learning, and the performance of the model is guaranteed.
Owner:NANJING UNIV

High-dimensional multi-label data flow classification method based on online sequence kernel extreme learning machine

The invention discloses a multi-label text data stream classification method based on an online sequence kernel extreme learning machine. The method comprises the steps that 1, constructing a BoW model and a sliding window mechanism according to an external corpus to divide multi-label text data streams into data blocks and then vectorize the data blocks; 2, predicting the text data block Dk at the moment k by utilizing the integrated classifier model at the moment k1, and outputting a prediction result; 3, performing feature selection on the text feature set of the text data block Dk to obtain a dimension-reduced text feature set Mk; 4, judging whether concept drift or feature drift occurs or not according to the cosine similarity between the class label spaces of the text data block Dk at the moment k and the text data block Dk1 at the moment k1 and the distribution difference between the feature sets after dimension reduction; 5, constructing an online sequence kernel extreme learning machine by utilizing all texts in the text data block Dk according to the drift detection condition, and updating to an integrated classifier model at the moment k. According to the method, the classification problem of the multi-label text data flow with feature drift and concept drift is solved.
Owner:HEFEI UNIV OF TECH

Concept drift detection method based on classifier diversity and Mcdiarmid inequality

The invention discloses a concept drift detection method based on classifier diversity and a Mcdiarmid inequality. The objective of the invention is to detect whether conceptual drift occurs in a datastream by combining the inconsistency of a plurality of classifiers and the Mcdiarmid inequality. The method comprises the following steps: 1, incrementally training two individual classifiers with relatively large divergence, monitoring the diversity of the pair of classifiers for a newly coming data stream, and calculating the difference measurement of prediction results between the classifiers; 2, setting the size of the sliding window h to be n, and if the sliding window h is not full, automatically adding the difference measurement result of the latest data stream into the sliding windowh; and if the sliding window h is full, moving the initial difference measurement result out of the sliding window, and adding the latest result; 3, giving a confidence coefficient, and solving a threshold value for judging drifting through the confidence coefficient and an Mcdiarmid inequality theory. 4, associating each element in the sliding window with a weight, calculating the difference value between the weighted average value of the sliding window at the current moment and the maximum weighted average value observed at present, and comparing the difference value with the previously obtained threshold value to judge whether drifting occurs or not, so that concept drifting can be effectively detected, a classifier is updated, and better classification performance and generalization ability are shown.
Owner:NANJING UNIV OF AERONAUTICS & ASTRONAUTICS

Abnormal data flow online calibration system heurized by brain-like hierarchical memory mechanism

The invention relates to an abnormal data flow online calibration system heurized by a brain-like hierarchical memory mechanism. The system comprises a missing data filling module (I), a dimension reduction module (II), a multi-dimensional counting Bloom filter module (III), a hierarchical memory library module (IV), an experience knowledge base module (V) and a calibrated data block module (VI).The I is used for filling up missing data in data blocks in a data stream batch processing link. The II is used for performing low-dimensional representation on the high-dimensional data in the data block. The III is used for judging whether the new data sample is abnormal . The IV is used for storing the historical data sample processed in the step II. The V is used for storing historical data samples which are processed by I but not processed by II. The III and theIV are matched with each other to be replaced, and the IV and the V are matched with each other to be updated; and the VI is usedstoring the replaced and updated new data sample. According to the online calibration system, under the condition that original data distribution is not changed, outliers, noise, missing values and the self-adaptive concept drift phenomenon are corrected in real time.
Owner:DONGHUA UNIV

Model adaptive training method and device, equipment, medium and program product

PendingCN113657501AImprove performance and stabilityTroubleshooting technical issues with adaptive trainingCharacter and pattern recognitionNeural architecturesManual annotationData set
The invention provides a model adaptive training method and device, equipment, a medium and a program product, and the method comprises the steps: obtaining all operation data of an original model during actual operation, and detecting a first concept drift value of the original model during actual operation according to the operation data; and then, according to the value model and the first concept drift value, distributing each operation data to a tagged data set and/or a non-tagged data set, and when the data size of the tagged data set is greater than or equal to a preset threshold value, utilizing an adaptive training model, and according to the tagged data set and the non-tagged data set, performing adaptive training on the original model to determine a new trained model. The technical problem of how to enable the AI model to carry out adaptive training under the condition that human intervention is as little as possible is solved. The technical effects that the manual annotation amount needed when the developers update the models is reduced, and the performance stability of the models is improved in the mode of integrating the multiple models are achieved.
Owner:JINGDONG CITY BEIJING DIGITS TECH CO LTD

Dynamic production environment anomaly monitoring system oriented to real-time data streams

The invention relates to a dynamic production environment anomaly monitoring system oriented to real-time data streams. The dynamic production environment anomaly monitoring system comprises a real-time data collection module (I), a single-node sparse discrete encoder generation module (II), a single-node real-time anomaly monitoring module (III), a full-process all-node parallel real-time monitoring module (IV) and a production mode updating module (V), wherein the I is used for collecting numerical values of all nodes and integrating the numerical values into a multi-dimensional sample; theII is used for generating a plurality of sparse discrete encoders, wherein each sparse discrete encoder is used for performing sparse discrete encoding on the numerical value of each node; the III isused for judging whether the value is abnormal; the IV is used for monitoring all nodes in parallel in real time, and identifying noise interference and concept drift of each node according to the decision matrix; and the V is used for changing the production mode when it is determined that concept drift occurs. The dynamic production environment abnormity monitoring system oriented to the real-time data flow can monitor system abnormity in real time and distinguish whether noise interference or production mode change exists from the system abnormity.
Owner:DONGHUA UNIV
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