Discriminative Linear Classifiers

Sufficient physical activity and restful sleep play a major role in the prevention and cure of many chronic conditions. Being able to proactively screen and monitor such chronic conditions would be a big step forward for overall health. The rapid increase in the popularity of wearable devices pro-vides a significant new source, making it possible to track the user’s lifestyle real-time. In this paper, we propose a novel unsupervised representation learning technique called activ-ity2vecthat learns and “summarizes” the discrete-valued ac-tivity time-series. It learns the representations with three com-ponents: (i) the co-occurrence and magnitude of the activ-ity levels in a time-segment, (ii) neighboring context of the time-segment, and (iii) promoting subject-invariance with ad-versarial training. We evaluate our method on four disorder prediction tasks using linear classifiers. Empirical evaluation demonstrates that our proposed method scales and performs better than many strong baselines. The adversarial regime helps improve the generalizability of our representations by promoting subject invariant features. We also show that using the representations at the level of a day works the best since human activity is structured in terms of daily routines.

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A Comparison of Linear and Non-linear Classifiers for the Detection of Coronary Artery Disease in Stress-ECG

Artificial Intelligence in Medicine - Lecture Notes in Computer Science ◽

10.1007/3-540-48720-4_24 ◽

1999 ◽

pp. 227-231 ◽

Cited By ~ 2

Author(s):

Georg Dorffner ◽

Ernst Leitgeb ◽

Heinz Koller

Keyword(s):

Coronary Artery Disease ◽

Coronary Artery ◽

Linear Classifiers ◽

Non Linear ◽

Artery Disease

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Tracking Sparse Linear Classifiers

IEEE Transactions on Neural Networks and Learning Systems ◽

10.1109/tnnls.2018.2877433 ◽

2019 ◽

Vol 30 (7) ◽

pp. 2079-2092 ◽

Cited By ~ 3

Author(s):

Tingting Zhai ◽

Frederic Koriche ◽

Hao Wang ◽

Yang Gao

Keyword(s):

Linear Classifiers

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Efficiency of Linear and Non-Linear Classifiers for Gas Identification from Electrocatalytic Gas Sensor

Metrology and Measurement Systems ◽

10.2478/mms-2013-0043 ◽

2013 ◽

Vol 20 (3) ◽

pp. 501-512 ◽

Cited By ~ 14

Author(s):

Paweł Kalinowski ◽

Łukasz Woźniak ◽

Anna Strzelczyk ◽

Piotr Jasinski ◽

Grzegorz Jasinski

Keyword(s):

Gas Sensor ◽

Gas Sensors ◽

Support Vector ◽

Voltammetric Sensor ◽

Linear Classifiers ◽

Lack Of Information ◽

The Family ◽

Gas Identification ◽

Auto Scaling ◽

Nonlinear Support

Abstract Electrocatalytic gas sensors belong to the family of electrochemical solid state sensors. Their responses are acquired in the form of I-V plots as a result of application of cyclic voltammetry technique. In order to obtain information about the type of measured gas the multivariate data analysis and pattern classification techniques can be employed. However, there is a lack of information in literature about application of such techniques in case of standalone chemical sensors which are able to recognize more than one volatile compound. In this article we present the results of application of these techniques to the determination from a single electrocatalytic gas sensor of single concentrations of nitrogen dioxide, ammonia, sulfur dioxide and hydrogen sulfide. Two types of classifiers were evaluated, i.e. linear Partial Least Squares Discriminant Analysis (PLS-DA) and nonlinear Support Vector Machine (SVM). The efficiency of using PLS-DA and SVM methods are shown on both the raw voltammetric sensor responses and pre-processed responses using normalization and auto-scaling

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Classification of Fake Product Ratings Using a Timeline Based Approach

International Journal of Business Administration and Management Research ◽

10.24178/ijbamr.2017.3.2.12 ◽

2017 ◽

Vol 3 (2) ◽

pp. 12 ◽

Cited By ~ 1

Author(s):

Neha Thomas ◽

Susan Elias

Keyword(s):

Language Processing ◽

Opinion Mining ◽

Optimal Point ◽

Linear Classifiers ◽

Wide Range ◽

Text Content ◽

Classification Tool ◽

Fake Reviews ◽

Product Ratings

Abstract— Detection of fake review and reviewers is currently a challenging problem in cyber space. It is challenging primarily due to the dynamic nature of the methodology used to fake the review. There are several aspects to be considered when analyzing reviews to classify them effective into genuine and fake. Sentiment analysis, opinion mining and intend mining are fields of research that try to accomplish the goal through Natural Language Processing of the text content of the review. In this paper, an approach that uses the review ratings evaluated along a timeline is presented. An Amazon dataset comprising of ratings indicated for a wide range of products was used for the analysis presented here. The analysis of the ratings was carried out for an electronic product over a period of six years. The computed average rating helps to identify linear classifiers that define solution boundaries within the dataspace. This enables a product specific classification of review ratings and suitable recommendations can also be generated automatically. The paper explains a methodology to evaluate the average product ratings over time and presents the research outcomes using a novel classification tool. The proposed approach helps to determine the optimal point to distinguish between fake and genuine ratings for each product. Index Terms: Fake reviews, Fake Ratings, Product Ratings, Online Shopping, Amazon Dataset.

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