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Barnan Das
Washington State University
17Publications
10H-index
439Citations
Publications 17
Newest
#1Barnan Das (WSU: Washington State University)H-Index: 10
#2Narayanan C. Krishnan (IIT-RPR: Indian Institute of Technology Ropar)H-Index: 15
Last.Diane J. Cook (WSU: Washington State University)H-Index: 54
view all 3 authors...
As machine learning techniques mature and are used to tackle complex scientific problems, challenges arise such as the imbalanced class distribution problem, where one of the target class labels is under-represented in comparison with other classes. Existing oversampling approaches for addressing this problem typically do not consider the probability distribution of the minority class while synthetically generating new samples. As a result, the minority class is not represented well which leads ...
#1Barnan Das (WSU: Washington State University)H-Index: 10
#2Narayanan C. Krishnan (WSU: Washington State University)H-Index: 15
Last.Diane J. Cook (WSU: Washington State University)H-Index: 54
view all 3 authors...
The area of supervised machine learning often encounters imbalanced class distribution problem where one class is under represented as compared to other classes. Additionally, in many real-life problem domains, data with an imbalanced class distribution contains ambiguous regions in the data space where the prior probability of two or more classes are approximately equal. This problem, known as overlapping classes, thus makes it difficult for the learners in classification task. In this chapter,...
Dec 1, 2013 in ICDM (International Conference on Data Mining)
#1Barnan Das (WSU: Washington State University)H-Index: 10
#2Narayanan C. Krishnan (WSU: Washington State University)H-Index: 15
Last.Diane J. Cook (WSU: Washington State University)H-Index: 54
view all 3 authors...
The class imbalance problem is a well-known classification challenge in machine learning that has vexed researchers for over a decade. Under-representation of one or more of the target classes (minority class(es)) as compared to others (majority class(es)) can restrict the application of conventional classifiers directly on the data. In addition, emerging challenges such as overlapping classes, make class imbalance even harder to solve. Class overlap is caused due to ambiguous regions in the dat...
Dec 1, 2013 in ICDM (International Conference on Data Mining)
#1Barnan Das (WSU: Washington State University)H-Index: 10
#2Narayanan C. Krishnan (WSU: Washington State University)H-Index: 15
Last.Diane J. Cook (WSU: Washington State University)H-Index: 54
view all 3 authors...
As machine learning techniques mature and are used to tackle complex scientific problems, challenges arise such as the imbalanced class distribution problem, where one of the target class labels is under-represented in comparison with other classes. Existing over sampling approaches for addressing this problem typically do not consider the probability distribution of the minority class while synthetically generating new samples. As a result, the minority class is not well represented which leads...
Oct 1, 2012 in UbiComp (Ubiquitous Computing)
#1Barnan Das (WSU: Washington State University)H-Index: 10
#2Diane J. Cook (WSU: Washington State University)H-Index: 54
Last.Adriana Seelye (WSU: Washington State University)H-Index: 15
view all 4 authors...
The growth in popularity of smart environments has been quite steep in the last decade and so has the demand for smart health assistance systems. A smart home-based prompting system can enhance these technologies to deliver in-home interventions to users for timely reminders or brief instructions describing the way a task should be carried out for successful completion. This technology is in high demand given the desire of people who have physical or cognitive limitations to live independently i...
Jun 1, 2012 in IE (Intelligent Environments)
#1Stefan DernbachH-Index: 1
#2Barnan Das (WSU: Washington State University)H-Index: 10
Last.Diane J. Cook (WSU: Washington State University)H-Index: 54
view all 5 authors...
Due to an increased popularity of assistive healthcare technologies activity recognition has become one of the most widely studied problems in technology-driven assistive healthcare domain. Current approaches for smart-phone based activity recognition focus only on simple activities such as locomotion. In this paper, in addition to recognizing simple activities, we investigate the ability to recognize complex activities, such as cooking, cleaning, etc. through a smart phone. Features extracted f...
Jan 1, 2012 in CCNC (Consumer Communications and Networking Conference)
#1Barnan Das (WSU: Washington State University)H-Index: 10
#2Adriana Seelye (WSU: Washington State University)H-Index: 15
Last.Maureen Schmitter-Edgecombe (WSU: Washington State University)H-Index: 29
view all 6 authors...
Individuals with cognitive impairment have difficulty successfully performing activities of daily living, which can lead to decreased independence. In order to help these individuals age in place and decrease caregiver burden, technologies for assistive living have gained popularity over the last decade. In this work, a context-aware prompting system is implemented, augmented by a smart phone to determine prompt situations in a smart home environment. While context-aware systems use temporal and...
Jan 1, 2012 in CCNC (Consumer Communications and Networking Conference)
#1Barnan Das (WSU: Washington State University)H-Index: 10
#2Brian L. Thomas (WSU: Washington State University)H-Index: 7
Last.Maureen Schmitter-Edgecombe (WSU: Washington State University)H-Index: 29
view all 6 authors...
Individuals with cognitive impairment have difficulty successfully performing activities of daily living, which can lead to decreased independence. In order to help these individuals age in place and decrease caregiver burden, technologies for assistive living have gained popularity over the last decade. This demo illustrates the implementation of a context-aware prompting system augmented by a smart phone to determine prompt situations in a smart home environment. While context-aware systems us...
Jan 1, 2012 in AmI (Ambient Intelligence)
#1Barnan Das (WSU: Washington State University)H-Index: 10
#2Narayanan C. Krishnan (WSU: Washington State University)H-Index: 15
Last.Diane J. Cook (WSU: Washington State University)H-Index: 54
view all 3 authors...
Over the last decade there has been a significant growth of research endeavors in the area of ambient intelligence or smart environments. An anticipated increase in the older adult population around the globe and an increase in health care expenditures as a result, has increased the demand of smart health assistance systems. Along with the classical problems of remote health monitoring and activity tracking, delivering in-home interventions to residents for timely reminders or brief instructions...
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