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In dynamic ensemble selection (DES) techniques, the competence level of each classifier is estimated from a pool of classifiers, and only the most competent ones are selected to classify a specific test sample and predict its class labels. A ...

The k nearest neighbor (KNN) classifier is one of the well-known instance-based classifiers. Nevertheless, the low efficiency in both running time and memory usage is a great challenge in the KNN classifier and its improvements due to noise and ...

In multi-label learning, the training data is typically large-scale and contains numerous noisy and redundant instances. Directly inducing a classifier with raw data can result in higher memory overhead and lower classification performance. One ...

Applying ensemble learning to facial expression recognition is an important research field nowadays, but all may not be better than many, the redundant learners in the classifier pool may hinder the ensemble system’s performance, so ensemble ...

k-nearest neighbor classifier (KNN) is one of the most famous classification models due to its straightforward implementation and an error bounded by twice the Bayes error. However, it usually degrades because of noise and the high cost in computing the ...

The training data in multi-label learning are often high dimensional and contains a quantity of noise and redundant information, resulting in high memory overhead and low classification performance during the learning process. Therefore, ...

We propose a local–global classification scheme in which the feature space is, in a first phase, segmented by an unsupervised algorithm allowing, in a second phase, the application of distinct classification methods in each of the generated sub-...

Machine learning algorithms spend a lot of time processing data because they are not fast enough to commit huge data sets. Instance selection algorithms especially aim to tackle this trouble. However, even instance selection algorithms can suffer ...

Anomaly detection in time series is an important task to many applications, e.g, the maintenance policies of rotating machines within industries strongly rely on time series monitoring. Rotating machines are vital elements within ...

• Learning features for anomaly detection problems may be a challenging task.
• ...

K-Nearest Neighbors (KNN) rule is a simple yet powerful classification technique in machine learning. Nevertheless, it suffers from some drawbacks such as high memory consumption, low time efficiency, class overlapping ...

Due to the increasing size of the datasets, prototype selection techniques have been applied for reducing the computational resources involved in data mining and machine learning tasks. In this paper, we propose a density-based approach for ...

We propose a novel approach to anomaly detection called Curvature Anomaly Detection (CAD) and Kernel CAD based on the idea of polyhedron curvature. Using the nearest neighbors for a point, we consider every data point as the vertex of a ...

We employ random geometric digraphs to construct semi-parametric classifiers. These data-random digraphs belong to parameterized random digraph families called proximity catch digraphs (PCDs). A related geometric digraph family, class cover catch ...${\mathbb{}}^{}$$\mathrm{}$${\mathbb{}}^{}$

• Representative prototype selection facilitates the interpretability of aggregated representations.
• The discriminability of aggregated representations is strengthened by enforcing domain-invariant projection of bundled descriptors along ...

Given a set of hand-crafted local features, acquiring a global representation via aggregation is a promising technique to boost computational efficiency and improve task performance. Existing feature aggregation (FA) approaches, including Bag of ...

Multi-label classification problem is one of the most general and relevant problems in the area of classification, where each item of the evaluated dataset is associated with more than one label. This paper discusses novel algorithms for prototype ...

Prototype selection is one of the most common preprocessing tasks in data mining applications. The vast amounts of data that we must handle in practical problems render the removal of noisy, redundant or useless instances a convenient ...

• We propose a new method for combining prototype selection algorithms.
• The ...

• A new prototype selection for multi-output regression data sets is presented.
• A ...

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A novel approach to prototype selection for multi-output regression data sets is presented. A multi-objective evolutionary algorithm is used to evaluate the selections using two criteria: training data set compression and prediction ...

• K-means clustering algorithm can be significantly improved by using a better initialization technique, and by repeating (re-starting) the algorithm.

In this paper, we study what are the most important factors that deteriorate the performance of the k-means algorithm, and how much this deterioration can be overcome either by using a better initialization technique, or by repeating (...

The offline Handwritten Signature Verification (HSV) problem can be considered as having difficult data since it presents imbalanced class distributions, high number of classes, high-dimensional feature space and small number of learning samples. ...

Because of the ever growing amounts of data, challenges have appeared for storage and processing, making data reduction still an important field of study. Numerosity reduction or prototype selection is one of the primary methods of data reduction. ...