StreamSoNG: A Soft Streaming Classification Approach

Wang, Wenlong; Keller, James M.; Dale, Jeffrey; Bezdek, James C.

doi:10.1109/tetci.2021.3097740

Cited by 8 publications

(8 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The first expression of a possibilistic KNN, shown in equation (18), worked well for a two-class application. In the streaming scenario, the number of classes is unknown.…”

Section: Possibilistic K-nearest Neighborsmentioning

confidence: 99%

“…In this work, I designed two multi-dimensional (multi-D) streaming processing algorithms: (i) Sequential Possibilistic Gaussian Mixture Model (SPGMM) that extends the Gaussian Mixture Model (GMM) into a temporal framework to track the pattern changes in data streams [17] and (ii) Streaming Soft Neural Gas (StreamSoNG) that extends the Neural Gas (NG) and Possibilistic K-Nearest Neighbors (PKNN) into a temporal framework to track the pattern changes in data streams [18]. A major contribution of this work is that trajectory analysis on data streams is defined and conducted upon SPGMM to offer earlier, more sensitive health alerts that are customized to the individual's health trajectory, with fewer false alarms.…”

Section: Chapter 1 Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Early detection of health changes in the elderly using in-home multi-sensor data streams

Wang¹

Self Cite

View full text Add to dashboard Cite

The rapid aging of the population worldwide requires increased attention from health care providers and the entire society. For the elderly to live independently, many health issues related to old age, such as frailty and risk of falling, need increased attention and monitoring. When monitoring daily routines for older adults, it is desirable to detect the early signs of health changes before serious health events, such as hospitalizations, happen, so that timely and adequate preventive care may be provided. By deploying multi-sensor systems in homes of the elderly, we can track trajectories of daily behaviors in a feature space defined using the sensor data. In this work, we investigate a methodology for learning data distribution from streaming data and tracking the evolution of the behavior trajectories over long periods (years) using high dimensional streaming clustering and provide very early indicators of changes in health. If we assume that habitual behaviors correspond to clusters in feature space and diseases produce a change in behavior, albeit not highly specific, tracking trajectory deviations can provide hints of early illness. Retrospectively, we visualize the streaming clustering results and track how the behavior clusters evolve in feature space with the help of two dimension-reduction algorithms, Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE). Moreover, our tracking algorithm in the original high dimensional feature space generates early health warning alerts if a negative trend is detected in the behavior trajectory. We validated our algorithm on synthetic data, real-world data and tested it on a pilot dataset of four TigerPlace residents monitored with a collection of motion, bed, and depth sensors over ten years. We used the TigerPlace electronic health records (EHR) to understand the residents' behavior patterns and to evaluate and explain the health warnings generated by our algorithm. The results obtained on the TigerPlace dataset show that most of the warnings produced by our algorithm can be linked to health events documented in the EHR, providing strong support for a prospective deployment of the approach.

show abstract

“…The first expression of a possibilistic KNN, shown in equation (18), worked well for a two-class application. In the streaming scenario, the number of classes is unknown.…”

Section: Possibilistic K-nearest Neighborsmentioning

confidence: 99%

Section: Chapter 1 Introductionmentioning

confidence: 99%

Early detection of health changes in the elderly using in-home multi-sensor data streams

Wang¹

Self Cite

View full text Add to dashboard Cite

show abstract

“…The result has the potential to significantly reduce the time and cost required to label large low altitude aerial data sets and build ML/AI models on specialized domains that have insufficient labeled training data. To begin, Chapter 3 details the first application of streaming soft neural gas (StreamSoNG) [12] on a streaming computer vision task [13]. To date, StreamSoNG has been developed using a combination of theory, controlled synthetic data sets (e.g., mixtures of Gaussians with noise), and real-world texture image data sets.…”

Section: Contributionsmentioning

confidence: 99%

“…Herein, we make the following specific contributions. This is the first application of StreamSoNG [12] on a real streaming computer vision task. To date, StreamSoNG has been developed using a combination of theory, controlled synthetic data sets (e.g., mixtures of Gaussians with noise), and real-world texture image data sets.…”

Section: Experiments 4: Dimensionality Reduction Technique Assessmentmentioning

confidence: 99%

See 1 more Smart Citation

Human-assisted self-supervised labeling of large data sets

Schulz¹

View full text Add to dashboard Cite

There is a severe demand for, and shortage of, large accurately labeled datasets to train supervised computational intelligence (CI) algorithms in domains like unmanned aerial systems (UAS) and autonomous vehicles. This has hindered our ability to develop and deploy various computer vision algorithms in/across environments and niche domains for tasks like detection, localization, and tracking. Herein, I propose a new human-in-the-loop (HITL) based growing neural gas (GNG) algorithm to minimize human intervention during labeling large UAS data collections over a shared geospatial area. Specifically, I address human driven events like new class identification and mistake correction. I also address algorithm-centric operations like new pattern discovery and self-supervised labeling. Pattern discovery and identification through self-supervised labeling is made possible through open set recognition (OSR). Herein, I propose a classifier with the ability to say "I don't know" to identify outliers in the data and bootstrap deep learning (DL) models, specifically convolutional neural networks (CNNs), with the ability to classify on N+1 classes. The effectiveness of the algorithms are demonstrated using simulated realistic ray-traced low altitude UAS data from the Unreal Engine. The results show that it is possible to increase speed and reduce mental fatigue over hand labeling large image datasets.

show abstract

A Proposed Methodology for Risk Classification Using Fuzzy Group Decision Making and Fuzzy C-Means

Yiğit

DÖNMEZ

2022

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

StreamSoNG: A Soft Streaming Classification Approach

Cited by 8 publications

References 45 publications

Early detection of health changes in the elderly using in-home multi-sensor data streams

Early detection of health changes in the elderly using in-home multi-sensor data streams

Human-assisted self-supervised labeling of large data sets

A Proposed Methodology for Risk Classification Using Fuzzy Group Decision Making and Fuzzy C-Means

Contact Info

Product

Resources

About