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Estimating the class prior for positive and unlabelled data via logistic regression
In the paper, we revisit the problem of class prior probability estimation with positive and unlabelled data gathered in a single-sample scenario....
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On semi-supervised learning
Major efforts have been made, mostly in the machine learning literature, to construct good predictors combining unlabelled and labelled data. These...
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Unobserved classes and extra variables in high-dimensional discriminant analysis
In supervised classification problems, the test set may contain data points belonging to classes not observed in the learning phase. Moreover, the...
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Neural Network for the Statistical Process Control of HVAC Systems in Passenger Rail Vehicles
In the rail industry, coach temperature regulation has become a crucial task to improve passenger thermal comfort. Over the past few years, European...
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Threshold-based Naïve Bayes classifier
The Threshold-based Naïve Bayes (Tb-NB) classifier is introduced as a (simple) improved version of the original Naïve Bayes classifier. Tb-NB...
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On smoothing and scaling language model for sentiment based information retrieval
Sentiment analysis or opinion mining refers to the discovery of sentiment information within textual documents, tweets, or review posts. This field...
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Topic based quality indexes assessment through sentiment
This paper proposes a new methodology called TOpic modeling Based Index Assessment through Sentiment (TOBIAS). This method aims at modeling the...
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Suggestions for combining psychometric-based and supervised classification methods to detect cheating in online exams
In recent years, with the spread of large-scale online exams, the need for new methodological approaches to detect test cheating has increased. There...
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Semi-supervised adapted HMMs for P2P credit scoring systems with reject inference
The majority of current credit-scoring models, used for loan approval processing, are generally built on the basis of the information from the...
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Topic and Sentiment Modelling for Social Media
This chapter presents an overview of topic and sentiment analysis approaches, as applied to social media posts (such as on Facebook or Twitter). We...
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Mini-batch learning of exponential family finite mixture models
Mini-batch algorithms have become increasingly popular due to the requirement for solving optimization problems, based on large-scale data sets....
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Nonparametric semi-supervised classification with application to signal detection in high energy physics
Model-independent searches in particle physics aim at completing our knowledge of the universe by looking for new possible particles not predicted by...
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Core Clustering as a Tool for Tackling Noise in Cluster Labels
Real-world data sets often contain mislabelled entities. This can be particularly problematic if the data set is being used by a supervised...
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On Fractionally-Supervised Classification: Weight Selection and Extension to the Multivariate t-Distribution
Recent work on fractionally-supervised classification (FSC), an approach that allows classification to be carried out with a fractional amount of...
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An apparent paradox: a classifier based on a partially classified sample may have smaller expected error rate than that if the sample were completely classified
There has been increasing interest in using semi-supervised learning to form a classifier. As is well known, the (Fisher) information in an...
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A Semi-parametric Density Estimation with Application in Clustering
The idea behind density-based clustering is to associate groups to the connected components of the level sets of the density of the data to be...
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Classification of Events Using Local Pair Correlation Functions for Spatial Point Patterns
Spatial point pattern analysis usually concerns identifying features in an observation window where there is also noise. This identification...
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Clustering
The goal of this chapter is to survey and present the main concepts and techniques from the vast collection of clustering models, from the...
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Analysis of conditional randomisation and permutation schemes with application to conditional independence testing
We study properties of two resampling scenarios: Conditional Randomisation and Conditional Permutation schemes, which are relevant for testing...
