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Efficient three-way SVM for three-class classification problems
Many classification problems in the real world are inherently multi-class. However, most of the classifiers are binary. Solving K -class...
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Distributed independent vector machine for big data classification problems
In recent years, various studies have been conducted on SVMs and their applications in different area. They have been developed significantly in many...
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ELM: a novel ensemble learning method for multi-target regression and multi-label classification problems
In this paper, a new Ensemble Learning Method (ELM) is proposed to deal with multi-target regression and multi-label classification problems. In ELM,...
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Fortified Cuckoo Search Algorithm on training multi-layer perceptron for solving classification problems
Multi-layer perceptron (MLP) in artificial neural networks (ANN) is one among the trained neural models which can hold several layers as a hidden...
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Improving the Generalisation Ability of Neural Networks Using a Lévy Flight Distribution Algorithm for Classification Problems
While multi-layer perceptrons (MLPs) remain popular for various classification tasks, their application of gradient-based schemes for training leads...
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Companion Classification Losses for Regression Problems
By their very nature, regression problems can be transformed into classification problems by discretizing their target variable. Within this...
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General Performance Score for classification problems
Several performance metrics are currently available to evaluate the performance of Machine Learning (ML) models in classification problems. ML models...
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Triplet MAML for Few-Shot Classification Problems
In this study, we propose a TripletMAML algorithm as an extension to Model-Agnostic Meta-Learning (MAML) which is the most widely-used...
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Ultra Fast Classification and Regression of High-Dimensional Problems Projected on 2D
We propose the two-dimensional visual map classifier and regressor, which project the high-dimensional patterns on a 2D map, for human visualization...
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Combining Text and Image Analysis Methods for Solving Multimodal Classification Problems
AbstractFairly large number of recent studies are devoted to the analysis of data containing heterogeneous information. Multimodality is considered...
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Fuzzy Pattern Trees for Classification Problems Using Genetic Programming
Fuzzy Pattern Trees (FPTs) are tree-based structures in which the internal nodes are fuzzy operators, and the leaves are fuzzy features. This work...
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The Problems and Methods of Automatic Text Document Classification
AbstractThis paper gives a review of the main problems and methods of automatic text classification. It focuses on problems such as the choice of...
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A multi-manifold learning based instance weighting and under-sampling for imbalanced data classification problems
Under-sampling is a technique to overcome imbalanced class problem, however, selecting the instances to be dropped and measuring their...
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Identification and classification of heat pump problems in the field and their implication for a user-centric problem recognition
Heat pumps are at the heart of the transition to sustainable heating in buildings. Yet, minor installation and setting errors add to unnoticed...
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An experimental study: using categorical or fuzzy inputs for classification problems with dimensionality reduction
A fuzzy control system is a mathematical framework that evaluates analog input data in terms of logical variables with continuous values ranging from...
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Sub-SpaCE: Subsequence-Based Sparse Counterfactual Explanations for Time Series Classification Problems
The interpretation of existing machine learning models has become a critical task to facilitate the widespread adoption of AI across different...
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Imbalance factor: a simple new scale for measuring inter-class imbalance extent in classification problems
Learning from datasets that suffer from differences in absolute frequency of classes is one of the most challenging tasks in the machine learning...
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Learning the transfer function in binary metaheuristic algorithm for feature selection in classification problems
One of the most challenging issues in pattern recognition is the data attribution selection process. Feature selection plays a key role in solving...
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A multi-label classification system for anomaly classification in electrocardiogram
Automatic classification of ECG signals has become a research hotspot, and most of the research work in this field is currently aimed at single-label...
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Learning label-specific features for decomposition-based multi-class classification
Multi-class classification can be solved by decomposing it into a set of binary classification problems according to some encoding rules, e.g.,...