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Iterative threshold-based Naïve bayes classifier
The iterative Threshold-based Naïve Bayes (iTb-NB) classifier is introduced as a (simple) improved version of the previously introduced non-iterative...
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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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Do Prior Information on Performance of Individual Classifiers for Fusion of Probabilistic Classifier Outputs Matter?
In this paper, a class of classifier fusion methods are compared to verify the impact of the use of some prior information about individual...
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The Naive Bayes Classifier
The Naive Bayes Classifier makes a so-called conditional independence assumption that is almost always wrong. This incorrect assumption earns the...
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High-dimensional penalized Bernstein support vector classifier
The support vector machine (SVM) is a powerful classifier used for binary classification to improve the prediction accuracy. However, the...
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A topological data analysis based classifier
Topological Data Analysis (TDA) is an emerging field that aims to discover a dataset’s underlying topological information. TDA tools have been...
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Fair evaluation of classifier predictive performance based on binary confusion matrix
Evaluating the ability of a classifier to make predictions on unseen data and increasing it by tweaking the learning algorithm are two of the main...
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Notes on the H-measure of classifier performance
The H-measure is a classifier performance measure which takes into account the context of application without requiring a rigid value of relative...
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RR-classifier: a nonparametric classification procedure in multidimensional space based on relative ranks
Notions of data depth have motivated nonparametric multivariate analysis, especially in supervised learning. Maximum depth classifiers, classifiers...
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Extreme value theory for anomaly detection – the GPD classifier
Classification tasks usually assume that all possible classes are present during the training phase. This is restrictive if the algorithm is used...
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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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Analysis of estimating the Bayes rule for Gaussian mixture models with a specified missing-data mechanism
Semi-supervised learning approaches have been successfully applied in a wide range of engineering and scientific fields. This paper investigates the...
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A New Matrix Feature Selection Strategy in Machine Learning Models for Certain Krylov Solver Prediction
Numerical simulation processes in scientific and engineering applications require efficient solutions of large sparse linear systems, and variants of...
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Classification Under Partial Reject Options
In many applications there is ambiguity about which (if any) of a finite number N of hypotheses that best fits an observation. It is of interest then...
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A subspace aggregating algorithm for accurate classification
We present a technique for learning via aggregation in supervised classification. The new method improves classification performance, regardless of...
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Sensitivity and Specificity versus Precision and Recall, and Related Dilemmas
Many evaluations of binary classifiers begin by adopting a pair of indicators, most often sensitivity and specificity or precision and recall....
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Neural networks with functional inputs for multi-class supervised classification of replicated point patterns
A spatial point pattern is a collection of points observed in a bounded region of the Euclidean plane or space. With the dynamic development of...
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Exploring Dialog Act Recognition in Open Domain Conversational Agents
Recognizing dialog acts of users is an essential component in building successful conversational agents. In this work, we propose a dialog act (DA)...
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Semi-supervised sentiment clustering on natural language texts
In this paper, we propose a semi-supervised method to cluster unstructured textual data called semi-supervised sentiment clustering on natural...
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Predicting Item Characteristic Curve (ICC) Using a Softmax Classifier
The objective of item difficulty modeling (IDM) is to predict the statistical parameters of an item (e.g., difficulty) based on features extracted...