International Business Machines Corporation

Xue Ying Zhang

Jing James Xu

Si Er Han

Xiao Ming Ma

Ji Hui Yang

Jing Xu

Jun Wang

Wen Pei Yu

Steven G Barbee

Xing Wei

Yang Liu

Sheng Yan Sun

Li Jiang

Ning Zhang

Lei Gao

Jing Bo Jiang

Rui Wang

Yi Shao

Li Li Gu

Yan Yan Han

Liang Liang Dai

Yu Wang

Peng Hui Jiang

Jing Min Xu

Dong Hai Yu

Jian Jun Wang

Chuang Gan

Sha Li

Zhong Fang Yuan

Dakuo Wang

Huanlong Wu

Tong Liu

Lingfei Wu

Lei Tian

Jun Song Wang

Yang Liang

Hua Shao

Li Cao

Yuan Feng

Damir Spisic

Na Zhao

Xu Zhao

Kun Yan Yin

Weirui Liu

He Li

Yan Xa Zhou

Na Liu

Yi Ming Wang

Jin Wang

Zhaoyang Wu

Nanfei Chen

Ying Lin Xu

Tao Wen

Ze Ming Zhao

Zhaigu Fu

Ling Zhuo

Jianfeng Lin

Jiahua Liu

Song Bo

Chen Yu Chang

Linjun Yu

Bin Shang

Jijian Zhang

Min Zhang

Xusheng Chen

Huihui Wang

Jiaying Tan

Fei Qi

Kangquan Xu

Yi Wang

Jinmei Feng

Xuye Liu

Qinhong Chen

Binglei Niu

Xuxiang He

Yang Guo

Qingfeng Zhang

Junyi Zhou

Luan Qin

Jingjing Wang

Yi Shen

Yun Bo Zhang

Lin Yan Wu

Wei Shan Ws Kang

Wenbing Ling

Chunyue Xu

Chunqing Hu

Jianming Guan

Ningning Han

Qingqing Cai

Jing Jia

Xiao Li Zhang

Ziyue Huang

Yulong Li

Han Zhang

Fei Fan

Ang Chang

Su Li Hou

Tian Jiao Zhang

Hua Xa Li

Data segmentation, analysis, and security is provided. An analysis of a set of generated data evolution paths corresponding to a set of data collected over a defined span of time is performed. A behavior trend is determined based on analysis of the set of generated data evolution paths. A set of action steps is performed automatically based on the determined behavior trend.

Taking action based on data evolution

A method, system, and computer program product for incremental machine learning for a parametric machine learning model are disclosed. The method may include processing samples comprising historical samples and new samples with an existing parametric machine learning model to obtain at least one prediction residual of each of the samples, wherein the existing parametric machine learning model was trained based on the historical samples. The method may further include clustering the samples based on the at least one prediction residual of each of the samples and features of each of the samples. The method may further include sampling samples in each cluster to ensure that each cluster includes substantially similar number of sampled samples. The method may further include updating the existing parametric machine learning model to obtain an updated parametric machine learning model based on sampled samples in each cluster.

Incremental machine learning for a parametric machine learning model

A method, a computer system, and a computer program product for receiving a plurality of unreduced training data records (TDRs), using a first autoencoder on the plurality of unreduced TDRs to obtain a first plurality of reduced TDRs, clustering the first plurality of reduced TDRs into a plurality of clusters, wherein each first reduced TDR cluster is assigned a respective cluster label, transferring the respective cluster label from each first reduced TDR cluster to a corresponding unreduced TDR cluster, and performing stratified sampling to form K data blocks of unreduced TDRs from a plurality of unreduced TDR clusters.

Dimension reduction in the context of unsupervised learning

One or more computer processors extract respective features for each inter-modal sample in an inter-modal dataset, for each intra-modal sample in an intra-modal dataset, and a subsequent sample, wherein the inter-modal dataset and the intra-modal dataset are contained in a multi-modal training dataset. The one or more computer processors estimate an inter-modal label utilizing inter-modal label transformation of a subsequent sample. The one or more computer processors estimate an intra-modal label utilizing intra-modal label transformation of the subsequent sample. The one or more computer processors label the subsequent sample with a cross-modal label by combining the estimated inter-modal label and the estimated intra-modal label.

Cross-modal semi-supervised data labeling

A method, system, and computer for balance weighted voting. The method may comprise receiving, by a network interface, a scoring request. The method may further comprise, by a processing unit in response to the scoring request, generating a plurality of scores using a plurality of models. normalizing the plurality of scores, calculating an evaluation-based weighting factor from a first subset of the normalized scores, calculating a prediction-based based weighting factor from a second subset of the normalized scores, and calculating a balanced weighting predictor from the evaluation-based weighting factor and the prediction-based weighting factor. The method may further comprise returning, by the network interface, the balanced weighting predictor as an ensemble score for the scoring request.

Balance weighted voting

A computer-implemented method for anomaly detection for a time series data is provided. Aspects include receiving a time series data including a plurality of sequential data points, calculating an expected next value for the time series data based on the plurality of sequential data points, and receiving an actual next value corresponding to the time series data. Aspects also include calculating an anomaly strength estimate based on the expected next value and the actual next value, identifying one of a plurality of anomaly detection pipelines based on the anomaly strength estimate and a portrait associated with each of the plurality of anomaly detection pipelines, and obtaining an anomaly prediction by inputting the time series data and the actual next value into the one of the plurality of anomaly detection pipelines.

Anomaly detection for time series data

A method for improving a query performance of a query of a database application using rewrite includes introducing a database query into a database application to obtain a query result, analyzing the database query to identify index columns and a predicate column; identifying associated columns by determining if any associations exist between the index columns and the predicate column, calculating a confidence score value for each of the associated columns responsive to the associated columns and the predicate column, generating a list of associated columns with the confidence score values, generating a rewritten query by rewriting the database query based on the list of associated columns and the confidence score values and validating the rewritten query.

Query performance improvement of a query for a database application using rewrite

A computer-implemented method for treating post-modeling data includes computing, sequentially for each category of a feature, a category importance (CI) value. The CI value is based on a model accuracy change when records of a category being examined are reassigned to a remaining set of categories of the feature according to a cumulative distribution of records among the remaining set of categories of the feature, wherein the remaining set of categories include all categories of the feature, except for the category being examined. A post-modeling category is performed to merge of each category having the CI value less than a CI value threshold.

Key category identification and visualization

An approach to time-series data point anomaly detection may be presented. Data point anomalies in time-series data can cause a cascade of incorrect predictions in a time-series data prediction model. Presented herein may be an approach to decompose a time-series training data set into elementary components, such as seasonal, trend and residual. The approach may determine one or more confidence intervals for elementary components of data points including level shift, variance, and outlier. From these confidence intervals, new data points can be analyzed and identified as anomaly data points. The approach may also prevent anomaly data points from being incorporated into a time series data prediction model, reducing prediction error in the prediction model.

Optimization of time-series anomaly detection

In an approach for post-modeling data visualization and analysis, a processor presents a first visualization of a training dataset in a first plot. Responsive to receiving a selection of a data group of the training dataset to analyze, a processor identifies three or fewer key model features of the data group of the training dataset. A processor ascertains a representative record of each key model feature of the three or fewer key model features using a Local Interpretable Model-Agnostic Explanation technique. A processor presents a second visualization of the three or fewer key model features and the representative record of each key model feature in a second plot.

Visualize data and significant records based on relationship with the model

Embodiments analyze a query pattern of an incoming query on a database, perform a semantic analysis of the query pattern of the incoming query, generate a re-write query that has an improved query performance in comparison to a query performance of the incoming query based on the analyzed query pattern and the semantic analysis; build a query model using machine learning based on at least one of the query pattern and the semantic analysis; and apply the re-write query by performing the re-write query on the database to provide the improved query performance.

Query performance discovery and improvement

Display screen with transitional graphical user interface

A method, computer system, and a computer program product are provided for post-modeling feature evaluation. In one embodiment, at least at least one post model visual output and associated data is obtained that at least includes an individual conditional expectation (ICE) plot and a partial dependence (PDP) plot. Using the associated data and the plots, a Feature Importance (PI) plot is provided. A plurality of features is then determined for each PI, PDP and ICE plots to calculate at least one Interesting Value for each plot. An overall score is also calculated for each plurality of features based on the associated Interesting Values for each PDP, ICE and PI plots. At least one top feature is selected based on said scores. A final plot is then generated at least reflecting the top feature. The final plot combines the PI, PDP and ICE plots together.

Visual representation using post modeling feature evaluation

Disclosed are a computer-implemented method, a system and a computer program product for model exploration. Model feature importance of each model of a plurality of models can be obtained, the plurality of models can be grouped into a plurality of model clusters based on the model feature importance of each model, and the model feature importance can be presented by box-plot or confidence interval.

Feature importance based model optimization

Embodiments of the present disclosure relate to a method, system, and computer program product for efficient execution of a decision tree. According to the method, respective target values of a plurality of attributes of a target entity are obtained. Representations of a plurality of leaf nodes of a decision tree are obtained. Each of the representations indicates respective statistic values of a plurality of attributes of historical entities and a statistic prediction result determined from historical prediction results output at a respective one of the plurality of leaf nodes for the historical entities. Distance measures between the target entity and the plurality of leaf nodes are determined based on the target values and the statistic values indicated by the representations of the plurality of leaf nodes. A target prediction result for the target entity is determined based on the distance measures and the statistic prediction results of the historical entities.

Efficient execution of a decision tree

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Xi'an, China

Xue Ying Zhang