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Dimension reduction-based adaptive-to-model semi-supervised classification
This paper introduces a novel Dimension Reduction-based Adaptive-to-model Semi-supervised Classification method, specifically designed for scenarios...
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Maximizing adjusted covariance: new supervised dimension reduction for classification
This study proposes a new linear dimension reduction technique called Maximizing Adjusted Covariance (MAC), which is suitable for supervised...
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Supervised Classification of High-Dimensional Correlated Data: Application to Genomic Data
This work addresses the problem of supervised classification for high-dimensional and highly correlated data using correlation blocks and supervised...
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Supervised classification of spatial epidemics incorporating infection time uncertainty
Mechanistic models are key to providing reliable information for develo** infectious disease control strategies. In general, these models are...
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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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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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Supervised classification of curves via a combined use of functional data analysis and tree-based methods
Technological advancement led to the development of tools to collect vast amounts of data usually recorded at temporal stamps or arriving over time,...
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Overlap** coefficient in network-based semi-supervised clustering
Network-based Semi-Supervised Clustering (NeSSC) is a semi-supervised approach for clustering in the presence of an outcome variable. It uses a...
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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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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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Combining unsupervised and supervised learning techniques for enhancing the performance of functional data classifiers
This paper offers a supervised classification strategy that combines functional data analysis with unsupervised and supervised classification...
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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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Supervised Classification for Link Prediction in Facebook Ego Networks With Anonymized Profile Information
Social networks are very dynamic objects where nodes and links are continuously added or removed. Hence, an important but challenging task is link...
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A Novel Classification Algorithm Based on the Synergy Between Dynamic Clustering with Adaptive Distances and K-Nearest Neighbors
This paper introduces a novel supervised classification method based on dynamic clustering (DC) and K-nearest neighbor (KNN) learning algorithms,...
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Theory of angular depth for classification of directional data
Depth functions offer an array of tools that enable the introduction of quantile- and ranking-like approaches to multivariate and non-Euclidean...
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Performance Measures in Discrete Supervised Classification
The evaluation of results in Cluster Analysis frequently appears in the literature, and a variety of evaluation measures have been proposed. On the...
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Model-Based Clustering and Classification Using Mixtures of Multivariate Skewed Power Exponential Distributions
Families of mixtures of multivariate power exponential (MPE) distributions have already been introduced and shown to be competitive for cluster...
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Supervised Learning
Machine learning is going to play an ever-increasing role in the landscape of computational finance. The increasing market share of the ‘Algorithmic...
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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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