International Business Machines Corporation

Sony Corporation

Sony Electronics Inc

Deepak Srinivas Turaga

Srinivasan Parthasarathy

Olivier Verscheure

Nagui Halim

Daby Mousse Sow

Charu Chandra Aggarwal

Krishna Chaitanya Ratakonda

Venkata Nagaraju Pavuluri

Lisa Amini

Long H Vu

Anton Viktorovich Riabov

Wei Fan

Yuan-Chi Chang

Shirin Sohrabi Araghi

Horst Cornelius Samulowitz

Alain E Biem

Saket Sathe

Yanjie Fu

Jing Gao

Udayan Khurana

Mark C Johnson

Ali Jezu Tabatabai

Biplav Srivastava

Peter Hans Westerink

Octavian Udrea

Rohit Puri

Upendra V Chaudhari

Martin G Kienzle

Mark David Feblowitz

Gerald James Tesauro

Tamir Klinger

Chandrasekhara K Reddy

Gang Luo

Edward John Pring

Kartik Talamadupula

Timothy Rea Dinger

Justin Wai-chow Wong

Lydia Manikonda

Durga Sai Phaneendhar Vemuru

Ashish Sabharwal

Stuart S Horn

Edward W Shay

Fang Yuan

Peter Zimmer

Hsin I Tseng

Philip Shilung Yu

Joel Leonard Wolf

Laura Wynter

Yoonho Park

Henrique Andrade

Eric P Bouillet

Anand Ranganathan

James R Giles

Kirsten Weale Hildrum

Cesar Augusto Gonzales

Deepak Rajan

Chitra Venkatramani

Rohit Wagle

Vibhore Kumar

Laurent Sebastien Mignet

Perry G Hartswick

Yu Xuan Zhang

Brian Christopher Barker

Brian Douglas Pfeifer

Lingtao Cao

Amol N Ghoting

Elias Khalil

Edward Sumner Begle

Fatemeh Nargesian

Houping Xiao

Rodrigue Ngueyep Tzoumpe

Tan Hung M Ng

Kuldeep K Nigam

Swati Shah

Brian K Foo

Gerett Raybourn Yocum

Navendu Jain

Lars Kristen Selberg Daldorff

Raju Pavuluri

Embodiments for implementing enhanced ensemble model diversity and learning by a processor. One or more data sets may be created by combining one or more clusters of data points of a minority class with selected data points of a majority class. One or more ensemble models may be created from the one or more data sets using a supervised machine learning operation. An occurrence of an event may be predicted using the one or more ensemble models.

Enhanced ensemble model diversity and learning

Split an input dataset into training and test datasets; the former includes a plurality of data examples, each represented as a feature vector, and having an associated true label. Split the training dataset into a plurality of training data subsets; for each, train a corresponding machine learning model to obtain a plurality of such models, and apply same to the test dataset to obtain a plurality of predicted labels and prediction scores. For each of the plurality of examples, compute an agreement metric based on a corresponding one of the associated true labels; corresponding ones of the predicted labels; and corresponding ones of the prediction scores. Based on the computed metric, select, for at least some of the true label values, appropriate ones of the data examples to be added to a regression set. Add the appropriate ones of the data examples from the test dataset to the regression set.

Automated data and label creation for supervised machine learning regression testing

Embodiments for automated feature engineering by one or more processors are described. One or more selected transformations may be applied to a set of features in a dataset to create a set of transform features using random feature transformation forest (RFTF) classifiers. A transform feature may be selected from the set of transform features having a highest discriminative power as compared to other features of the set of transform features. At each node in a decision tree, store the selected feature, a split value, and the one or more selected transformations for the transform feature.

Random feature transformation forests for automatic feature engineering

Embodiments for feature engineering by one or more processors are described. A plurality of transformations are applied to a set of features in each of a plurality of datasets. An output of each of the plurality of transformations is a score. For each of the sets of features, selecting those of the plurality of transformations for which said score is above a predetermined threshold. A signal representative of said selection is generated.

Methods and systems for feature engineering

Techniques for scenario planning are provided. In one example, a computer-implemented method can comprise analyzing, by a device operatively coupled to a processor, content using a topic model. The content can be associated with a defined source and is related to one or more current events. The computer-implemented method can also comprise determining, by the device, one or more portions of the analyzed content that are relevant to one or more key risk drivers using a risk driver model. The computer-implemented method can also comprise aggregating, by the device, the determined one or more portions into one or more emerging storylines based on values of one or more attributes of the topic model.

Scenario planning and management

Scenario planning and risk management

Embodiments for accurate temporal event predictive modeling by a processor. An average reverse event delay may be determined from one or more event delays in a time-series window. A time-series event may be predicted by applying the average reverse event delay in conjunction with one or more weighted factors in a predictive model.

Accurate temporal event predictive modeling

An encoding system for encoding an event time series, the system including an inter-arrival time computing device configured to transform inter-arrival times between a plurality of input events into discrete time symbols and map the input events and the discrete time symbols using a dictionary to output a time gram representing a temporal dimension between a sequences of events.

Encoding for time grams

Techniques for autonomously generating a domain model and/or an action model based on unstructured data are provided. In one example, a computer implemented method can comprise extracting, by a system operatively coupled to a processor, a plurality of actions from a non-numerical language. The plurality of actions can achieve a goal. The computer-implemented method can also comprise generating, by the system, a domain model based on the plurality of actions. Further, the computer-implemented method can comprise generating, by the system, an action model based on the domain model. In various embodiments, the action model can comprise an action transition for accomplishing the goal.

Autonomous learning of actionable models from unstructured data

Autonomous learning of actionable models from unstrutured data

A system, method, and computer program product for automatically selecting from a plurality of analytic algorithms a best performing analytic algorithm to apply to a dataset is provided. The automatically selecting from the plurality of analytic algorithms the best performing analytic algorithm to apply to the dataset enables a training a plurality of analytic algorithms on a plurality of subsets of the dataset. Then, a corresponding prediction accuracy trend is estimated across the subsets for each of the plurality of analytic algorithms to produce a plurality of accuracy trends. Next, the best performing analytic algorithm is selected and outputted from the plurality of analytic algorithms based on the corresponding prediction accuracy trend with a highest value from the plurality of accuracy trends.

Automated intelligent data navigation and prediction tool

A method of analyzing physiological data streams. According to the method, physiological data is received into a computerized machine. The physiological data comprises numerical data and medical symptoms of a patient. Features are extracted from the physiological data based on development of the physiological data over a period of time. The features are converted into a textual representation using natural language generation. Input terms for an information retrieval system operating on the computerized machine are automatically generated based on the features. The input terms are input to the information retrieval system. A corpus of data is automatically searched to retrieve results to the input terms using the information retrieval system.

Hypothesis-driven, real-time analysis of physiological data streams using textual representations

Embodiments relate to analyzing dataset. A method of analyzing data is provided. The method obtains a description of a dataset. The method automatically generates a plurality of analysis options from the description of the dataset. The method generates a plurality of queries based on the analysis options. The method deploys the queries on the dataset to build a plurality of statistical models from the dataset.

Automatic enumeration of data analysis options and rapid analysis of statistical models

Growing community of inventors

Elmsford, NY, United States of America

Deepak Srinivas Turaga