Statistical Linear Models in Virus Genomic Alignment-free Classification: Application to Hepatitis C Viruses Machine Learning

Viral sequence classification is an important task in pathogen detection, epidemiological surveys and evolutionary studies. Statistical learning methods are widely used to classify and identify viral sequences in samples from environments. These methods face several challenges associated with the nature and properties of viral genomes such as recombination, mutation rate and diversity. Also, new generations of sequencing technologies rise other difficulties by generating massive amounts of fragmented sequences. While linear classifiers are often used to classify viruses, there is a lack of exploration of the accuracy space of existing models in the context of alignment free approaches. In this study, we present an exhaustive assessment procedure exploring the power of linear classifiers in genotyping and subtyping partial and complete genomes. It is applied to the Hepatitis C viruses (HCV). Several variables are considered in this investigation such as classifier types (generative and discriminative) and their hyper-parameters (smoothing value and penalty function), the classification task (genotyping and subtyping), the length of the tested sequences (partial and complete) and the length of k-mer words. Overall, several classifiers perform well given a set of precise combination of the experimental variables mentioned above. Finally, we provide the procedure and benchmark data to allow for more robust assessment of classification from virus genomes.

Partial AUC Maximization via Nonlinear Scoring Functions Machine Learning

We propose a method for maximizing a partial area under a receiver operating characteristic (ROC) curve (pAUC) for binary classification tasks. In binary classification tasks, accuracy is the most commonly used as a measure of classifier performance. In some applications such as anomaly detection and diagnostic testing, accuracy is not an appropriate measure since prior probabilties are often greatly biased. Although in such cases the pAUC has been utilized as a performance measure, few methods have been proposed for directly maximizing the pAUC. This optimization is achieved by using a scoring function. The conventional approach utilizes a linear function as the scoring function. In contrast we newly introduce nonlinear scoring functions for this purpose. Specifically, we present two types of nonlinear scoring functions based on generative models and deep neural networks. We show experimentally that nonlinear scoring fucntions improve the conventional methods through the application of a binary classification of real and bogus objects obtained with the Hyper Suprime-Cam on the Subaru telescope.

Data Science Simplified Part 10: An Introduction to Classification Models


The world around is full of classifiers. Classifiers help in preventing spam e-mails. Classifiers help in identifying customers who may churn. Classifiers help in predicting whether it will rain or not. This supervised learning method is ubiquitous in business applications.

Using Transfer Learning for NLP with Small Data


Text classification has numerous applications, from tweet sentiment, product reviews, toxic comments, and more. It's a popular project topic among Insight Fellows, however a lot of time is spent collecting labeled datasets, cleaning data, and deciding which classification method to use. Services like Clarifai, and Google AutoML have made it very easy to create image classification models with less labeled data, but it's not as easy to create such models for text classification. For image classification tasks, transfer learning has proven to be very effective in providing good accuracy with fewer labeled datasets. Transfer learning is a technique that enables the transfer of knowledge learned from one dataset to another.

A Hybrid Generative/Discriminative Approach to Semi-supervised Classifier Design

AAAI Conferences

Semi-supervised classifier design that simultaneously utilizes both labeled and unlabeled samples is a major research issue in machine learning. Existing semisupervised learning methods belong to either generative or discriminative approaches. This paper focuses on probabilistic semi-supervised classifier design and presents a hybrid approach to take advantage of the generative and discriminative approaches. Our formulation considers a generative model trained on labeled samples and a newly introduced bias correction model. Both models belong to the same model family. The proposed hybrid model is constructed by combining both generative and bias correction models based on the maximum entropy principle. The parameters of the bias correction model are estimated by using training data, and combination weights are estimated so that labeled samples are correctly classified. We use naive Bayes models as the generative models to apply the hybrid approach to text classification problems. In our experimental results on three text data sets, we confirmed that the proposed method significantly outperformed pure generative and discriminative methods when the classification performances of the both methods were comparable.