{"title":"Investigating the Application of Transfer Learning to Neural Time Series Classification","authors":"D. Kearney, S. McLoone, Tomas E. Ward","doi":"10.1109/ISSC.2019.8904960","DOIUrl":"https://doi.org/10.1109/ISSC.2019.8904960","url":null,"abstract":"Current approaches to EEG time series classification depend heavily on feature engineering to support the training of classifiers based on generalized linear models, decision trees, neural networks, or other machine learning techniques. This feature engineering demands considerable competence in mathematics, digital signal processing, statistics, linear algebra, etc. Researchers generating these time series often have clinical backgrounds, and may not be in a position to design and extract these features by hand. However, they are likely to be familiar with rudimentary - but fundamental - data visualisation methods. The objective of this paper is to investigate whether the application of transfer learning to such a classification problem can facilitate the replacement of involved feature engineering with straightforward data visualisation. While a classification accuracy of over 80% is achieved, the trained neural network exhibits the hallmarks of overfitting. We suggest alternative data visualisation techniques and modifications to the transfer learning method employed that may yield better results for multichannel neural time series data.","PeriodicalId":312808,"journal":{"name":"2019 30th Irish Signals and Systems Conference (ISSC)","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121524269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Comparison of Machine Learning Models in Food Authentication Studies","authors":"Manokamna Singh, Katarina Domijan","doi":"10.1109/ISSC.2019.8904924","DOIUrl":"https://doi.org/10.1109/ISSC.2019.8904924","url":null,"abstract":"The underlying objective of food authentication studies is to determine whether unknown food samples have been correctly labeled. In this paper, we study three near-infrared (NIR) spectroscopic datasets from food samples of different types: meat samples (labeled by species), olive oil samples (labeled by their geographic origin) and honey samples (labeled as pure or adulterated by different adulterants). We apply and compare a large number of classification, dimension reduction and variable selection approaches to these datasets. NIR data pose specific challenges to classification and variable selection: the datasets are high - dimensional where the number of cases $(n) < < mathbf{number}$ of features $(p)$ and the recorded features are highly serially correlated. In this paper, we carry out a comparative analysis of different approaches and find that partial least squares, a classic tool employed for these types of data, outperforms all the other approaches considered.","PeriodicalId":312808,"journal":{"name":"2019 30th Irish Signals and Systems Conference (ISSC)","volume":"242 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116518651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}