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About 10,000 papers for “multi-class classification”

Multi-class Classification without Multi-class Labels

Yen-Chang Hsu, Zhaoyang Lv, Joel Schlosser et al. · arXiv (Cornell University) · 2019

This work presents a new strategy for multi-class classification that requires no class-specific labels, but instead leverages pairwise similarity between examples, which is a weaker form of annotation. The proposed method, meta classification learning, optimizes a binary classifier for pairwise similarity prediction and through this process learns a multi-class classifier as a submodule. We formulate this approach, present a probabilistic graphical model for it, and derive a surprisingly simple loss function that can be used to learn neural network-based models. We then demonstrate that this same framework generalizes to the supervised, unsupervised cross-task, and semi-supervised settings. Our method is evaluated against state of the art in all three learning paradigms and shows a superior or comparable accuracy, providing evidence that learning multi-class classification without multi-class labels is a viable learning option.

Cited by 17Open access

LSTSVM-PBT Multi-class Classification

Qìng Yu, Lihui Wang · DEStech Transactions on Engineering and Technology Research · 2017

SVM is used to solve the problem of the binary-class classification. In view of the practical problems encountered in the multi-class classification problem, LSTSVMPBT (least squares support vector machines partial binary tree) is using partial binary tree to expand the basic category of classifier for multi-class SVM in this paper. Binary tree hierarchy solved the inseparable regional, improved the classification accuracy, reduced the training time. The time advantage is more obvious, especially the data set is large.

Cited by 1Open access

Learning multi-class classification problems

Timothy L. H. Watkin, Albrecht Rau, D. Bollé et al. · Journal de Physique I · 1992

A multi-class perceptron can learn from examples to solve problems whose answer may take several different values. Starting from a general formalism, we consider the learning of rules by a Hebbian algorithm and by a Monte-Carlo algorithm at high temperature. In the benchmark “prototype-problem” we show that a simple rule may be more than an order of magnitude more efficient than the well-known solution, and in the conventional limit is in fact optimal. A multi-class perceptron is significantly more efficient than a more complicated architecture of binary perceptrons.

Cited by 12Open access

Learning multi-class classification problems

Timothy L. H. Watkin, Albrecht Rau, Desiré Bollé et al. · Springer Link (Chiba Institute of Technology) · 1992

\nA multi-class perceptron can learn from examples to solve problems whose answer may take several different values. Starting from a general formalism, we consider the learning of rules by a Hebbian algorithm and by a Monte-Carlo algorithm at high temperature. In the benchmark “prototype-problem” we show that a simple rule may be more than an order of magnitude more efficient than the well-known solution, and in the conventional limit is in fact optimal. A multi-class perceptron is significantly more efficient than a more complicated architecture of binary perceptrons.\n

Cited by 0

Thyroid Disease Multi-Class Classification

Gaurav Singh, Vaishnavi V. Mane, Rishabh Anand et al. · Procedia Computer Science · 2025

Thyroid disease is a widespread and potentially serious medical condition affecting millions of people worldwide. Accurate diagnosis and classification of thyroid disorders is crucial for proper treatment and management of the disease. This work aimed to develop a robust machine learning model for classifying different thyroid conditions based on a comprehensive thyroid dataset. The dataset contained various features and parameters related to thyroid function, enabling the classification of patients into seven distinct thyroid condition categories. Three state-of-the-art machine learning algorithms were employed: Random Forest, Gradient Boosting, and Decision Tree. These ensemble methods are known for their strong predictive capabilities and ability to handle complex, non-linear relationships within the data. The models were rigorously trained and evaluated using appropriate techniques, including cross-validation and stratified sampling, to ensure reliable and generalizable results. Performance metrics such as the F2 score were utilized to assess the models’ performance and ability to handle class imbalances effectively. Through extensive experimentation and fine-tuning of hyperparameters, the Gradient Boosting model emerged as the top performer, achieving an impressive F2 score of 0.97. The model was trained using a comprehensive dataset and evaluated through 5-fold cross-validation, achieving an overall accuracy of 94.7%. Precision and recall scores were 93.5% and 92.8%, respectively, demonstrating the model’s strong performance. Comparative analysis with recent studies shows a 3.5% improvement in accuracy, confirming the efficacy of the proposed approach in thyroid disorder classification. Various other performance metrics based on the analyses of the multi-class confusion matrix resulting from the classification tests further elucidated the model’s strengths and weaknesses, providing valuable insights into its classification capabilities across different thyroid conditions. The findings of this work demonstrate the potential of machine learning to aid in thyroid disease diagnosis and classification, ultimately contributing to improved patient care and more informed clinical decision-making.

Cited by 2Open access

Multi-Space-Mapped SVMs for Multi-class Classification

Bo Liu, Longbing Cao, Philip S. Yu et al. · 2008

In SVMs-based multiple classification, it is not always possible to find an appropriate kernel function to map all the classes from different distribution functions into a feature space where they are linearly separable from each other. This is even worse if the number of classes is very large. As a result, the classification accuracy is not as good as expected. In order to improve the performance of SVMs-based multi-classifiers, this paper proposes a method, named multi-space-mapped SVMs, to map the classes into different feature spaces and then classify them. The proposed method reduces the requirements for the kernel function. Substantial experiments have been conducted on one-against-all, one-against-one, FSVM, DDAG algorithms and our algorithm using six UCI data sets. The statistical results show that the proposed method has a higher probability of finding appropriate kernel functions than traditional methods and outperforms others.

Cited by 10

Multi-class SVM for forestry classification

Nabil Hajj Chehade, Jean-Guy Boureau, Claude Vidal et al. · 2009

In this paper we propose a method for classifying the vegetation types in an aerial color infra-red (CIR) image. Different vegetation types do not only differ in color, but also in texture. We study the use of four Haralick features (energy, contrast, entropy, homogeneity) for texture analysis, and then perform the classification using the one-against-all (OAA) multi-class support vector machine (SVM), which is a popular supervised learning technique for classification. The choice of features (along with their corresponding parameters), the choice of the training set, and the choice of the SVM kernel highly affect the performance of the classification. The study was done on several CIR aerial images provided by the French National Forest Inventory (IFN). In this paper, we will show one example on a national forest near Sedan (in France), and compare our result with the IFN map.

Cited by 6

Multi-class Classification on "VINE" Structure

Somjet Suppharangsan, Mahesan Niranjan · 2011

In this paper we present a new One-Versus-All or OVA-based scheme for multi-class classification problems, aiming to reduce the training time when applying support vector machines (SVMs), particularly on large datasets. The experimental results on ten benchmark datasets show that the performance of the proposed scheme, referred to as "VINE", is comparable to that of its predecessor OVA scheme, but the former spends less training time than the latter scheme. On the problems with a large number of dimensions and instances, it is possible to combine VINE and a feature selection to obtain further speedup.

Cited by 0

ABC-LogitBoost for Multi-class Classification

Ping Li · ArXiv.org · 2009

We develop abc-logitboost, based on the prior work on abc-boost and robust logitboost. Our extensive experiments on a variety of datasets demonstrate the considerable improvement of abc-logitboost over logitboost and abc-mart.

Cited by 8Open access

Check-worthy multi-class classification results.

Juan R. Martinez-Rico (18990506), Lourdes Araujo (310347), Juan Martinez-Romo (310346) · Figshare · 2024

<div><p>Disinformation in the medical field is a growing problem that carries a significant risk. Therefore, it is crucial to detect and combat it effectively. In this article, we provide three elements to aid in this fight: 1) a new framework that collects health-related articles from verification entities and facilitates their check-worthiness and fact-checking annotation at the sentence level; 2) a corpus generated using this framework, composed of 10335 sentences annotated in these two concepts and grouped into 327 articles, which we call KEANE (faKe nEws At seNtence lEvel); and 3) a new model for verifying fake news that combines specific identifiers of the medical domain with triplets subject-predicate-object, using Transformers and feedforward neural networks at the sentence level. This model predicts the fact-checking of sentences and evaluates the veracity of the entire article. After training this model on our corpus, we achieved remarkable results in the binary classification of sentences (check-worthiness F1: 0.749, fact-checking F1: 0.698) and in the final classification of complete articles (F1: 0.703). We also tested its performance against another public dataset and found that it performed better than most systems evaluated on that dataset. Moreover, the corpus we provide differs from other existing corpora in its duality of sentence-article annotation, which can provide an additional level of justification of the prediction of truth or untruth made by the model.</p></div>

Cited by 0Open access

Multi‐class Classification and Adam Optimization

Resve Saleh, Sohaib Majzoub, A.K. Md. Ehsanes Saleh · 2025

This chapter addresses multi-class classification problems using neural networks and the details of their optimization during training. Loss functions for multi-class problems are described which require categorical versions of the cross-entropy and log-cosh loss functions. The sigmoid function is extended to incorporate multiple outputs, leading to the softmax activation function. The implementation of the softmax activation function is described in detail. The MNIST dataset is used as a demonstration vehicle to identify the advantages of using a robust loss function for problems with outliers in the dataset. In addition, a number of different algorithms that comprise the Adam optimization technique are described, including momentum and rmsprop.

Cited by 0Open access