| Summary: | This thesis is carried out in collaboration with a private company, DANSiC AB This study is an extension of a research work started by DANSiC AB in 2019 to classify a source. This study is about classifying a source into two classes with the sensitivity of one source higher than the other as one source has greater importance. The data provided for this thesis is based on sensor measurements on different temperature cycles. The data is high-dimensional and is expected to have a drift in measurements. Principal component analysis (PCA) is used for dimensionality reduction. “Differential”, “Relative” and “Fractional” drift compensation techniques are used for compensating the drift in data. A comparative study was performed using three different classification algorithms, which are “Linear Discriminant Analysis (LDA)”, “Naive Bayes classifier (NB)” and “Random forest (RF)”. The highest accuracy achieved is 59%,Random forest is observed to perform better than the other classifiers. === <p>This work is done with DANSiC AB in collaboration with Linkoping University.</p>
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