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About 7,711 papers for “Multiclass classification”

Multiclass Classification

Lutz Hamel · 2009

This chapter contains sections titled: One-Versus-the-Rest Classification Pairwise Classification Discussion Exercises Bibliographic Notes

Cited by 9

Multiclass classification experiments

Frank W. Marrs · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 2020

We seek a multiclass classifier that can satisfy the following requirements. 1. Predict among at least three classes. 2. Handle a moderately large number of correlated predictors. 3. Handle mixed categorical and continuous predictors. 4. Handle missing values (in some way). 5. Be trained with about n = 1,000 responses, and 6. Quantify prediction uncertainty. Based on 5. above, it is assumed that accuracy of prediction is paramount (relative to, say, runtime). All methods considered in this document take on the order of minutes (most take seconds) on a 2.8 GHz Quad-Core Intel Core i7 processor with 16GB RAM. Secondly, quantification of uncertainty is assumed to be a secondary task, since, with this small data size, resampling methods (i.e. bootstrapping) are possible. Further studies should quantify the feasibility and accuracy of resampling methods, and/or other methods, for quantifying uncertainty. R was used for all software.

Cited by 0Open access

Multiclass classification results.

Xiaoli Yang (314226), Jiayi Zhou (2228527), Chenchen Wang (12690) et al. · Figshare · 2025

<div> Alzheimer’s disease (AD) is a common type of dementia, with mild cognitive impairment (MCI) being a key precursor. Early MCI diagnosis is crucial for slowing AD progression, but distinguishing MCI from normal controls (NC) is challenging due to subtle imaging differences. Furthermore, differentiating early MCI (EMCI) from late MCI (LMCI) is also important for interventions. This study proposes a deep learning-based approach using a weighted probability-based ensemble method to integrate results from three-dimensional residual networks (3D ResNet). (1) This study employs 3D ResNet-18, 3D ResNet-34, and 3D ResNet-50 architectures with the Convolutional Block Attention Module (CBAM). The attention mechanism enhances performance by helping the model focus on pertinent information. Data augmentation techniques are applied to address limited data and improve accuracy. (2) To overcome the limitation of the individual convolutional neural network (CNN), an ensemble learning method is adopted. The method assigns weights to each 3D CNN model based on prediction accuracy and integrates them to obtain the final result. Our method achieves accuracy of 94.87%, 92.31%, 95.49%, and 95.97% for MCI vs. NC, MCI vs. AD, EMCI vs. LMCI, and NC vs. EMCI vs. LMCI vs. AD, respectively. The results demonstrate the effectiveness of our method for AD diagnosis. </div>

Cited by 0Open access

Multiclass classification strategies.

Arta Misini (19929042), Ercan Canhasi (17457899), Arbana Kadriu (19929045) et al. · Figshare · 2024

&lt;div&gt;&lt;p&gt;Automatic authorship identification is a challenging task that has been the focus of extensive research in natural language processing. Regardless of the progress made in attributing authorship, the need for corpora in under-resourced languages impedes advancing and examining present methods. To address this gap, we investigate the problem of authorship attribution in Albanian. We introduce a newly compiled corpus of Albanian newsroom columns and literary works and analyze machine-learning methods for detecting authorship. We create a set of hand-crafted features targeting various categories (lexical, morphological, and structural) relevant to Albanian and experiment with multiple classifiers using two different multiclass classification strategies. Furthermore, we compare our results to those obtained using deep learning models. Our investigation focuses on identifying the best combination of features and classification methods. The results reveal that lexical features are the most effective set of linguistic features, significantly improving the performance of various algorithms in the authorship attribution task. Among the machine learning algorithms evaluated, XGBoost demonstrated the best overall performance, achieving an F1 score of 0.982 on literary works and 0.905 on newsroom columns. Additionally, deep learning models such as fastText and BERT-multilingual showed promising results, highlighting their potential applicability in specific scenarios in Albanian writings. These findings contribute to the understanding of effective methods for authorship attribution in low-resource languages and provide a robust framework for future research in this area. The careful analysis of the different scenarios and the conclusions drawn from the results provide valuable insights into the potential and limitations of the methods and highlight the challenges in detecting authorship in Albanian. Promising results are reported, with implications for improving the methods used in Albanian authorship attribution. This study provides a valuable resource for future research and a reference for researchers in this domain.&lt;/p&gt;&lt;/div&gt;

Cited by 0Open access

Extreme Multiclass Classification Criteria

Anna Choromanska, Ish Kumar Jain · Computation · 2019

We analyze the theoretical properties of the recently proposed objective function for efficient online construction and training of multiclass classification trees in the settings where the label space is very large. We show the important properties of this objective and provide a complete proof that maximizing it simultaneously encourages balanced trees and improves the purity of the class distributions at subsequent levels in the tree. We further explore its connection to the three well-known entropy-based decision tree criteria, i.e., Shannon entropy, Gini-entropy and its modified variant, for which efficient optimization strategies are largely unknown in the extreme multiclass setting. We show theoretically that this objective can be viewed as a surrogate function for all of these entropy criteria and that maximizing it indirectly optimizes them as well. We derive boosting guarantees and obtain a closed-form expression for the number of iterations needed to reduce the considered entropy criteria below an arbitrary threshold. The obtained theorem relies on a weak hypothesis assumption that directly depends on the considered objective function. Finally, we prove that optimizing the objective directly reduces the multi-class classification error of the decision tree.

Cited by 0Open access

Multiclass threshold-based classification

Francesco Marchetti, Edoardo Legnaro, Sabrina Guastavino · ArXiv.org · 2025

In this paper, we introduce a threshold-based framework for multiclass classification that generalizes the standard argmax rule. This is done by replacing the probabilistic interpretation of softmax outputs with a geometric one on the multidimensional simplex, where the classification depends on a multidimensional threshold. This change of perspective enables for any trained classification network an a posteriori optimization of the classification score by means of threshold tuning, as usually carried out in the binary setting. This allows a further refinement of the prediction capability of any network. Moreover, this multidimensional threshold-based setting makes it possible to define score-oriented losses, which are based on the interpretation of the threshold as a random variable. Our experiments show that the multidimensional threshold tuning yields consistent performance improvements across various networks and datasets, and that the proposed multiclass score-oriented losses are competitive with standard loss functions, resembling the advantages observed in the binary case.

Cited by 0Open access

Web Page Multiclass Classification

Gaither, Brian, Debouse, Antonio, Huang, Catherine · SMU Scholar (Southern Methodist University) · 2022

As the internet age evolves, the volume of content hosted on the Web is rapidly expanding. With this ever-expanding content, the capability to accurately categorize web pages is a current challenge to serve many use cases. This paper proposes a variation in the approach to text preprocessing pipeline whereby noun phrase extraction is performed first followed by lemmatization, contraction expansion, removing special characters, removing extra white space, lower casing, and removal of stop words. The first step of noun phrase extraction is aimed at reducing the set of terms to those that best describe what the web pages are about to improve the categorization capabilities of the model. Separately, a text preprocessing using keyword extraction is evaluated. In addition to the text preprocessing techniques mentioned, feature reduction techniques are applied to optimize model performance. Several modeling techniques are examined using these two approaches and are compared to a baseline model. The baseline model is a Support Vector Machine with linear kernel and is based on text preprocessing and feature reduction techniques that do not include noun phrase extraction or keyword extraction and uses stemming rather than lemmatization. The recommended SVM One-Versus-One model based on noun phrase extraction and lemmatization during text preprocessing shows an accuracy improvement over the baseline model of nearly 1% and a 5-fold reduction in misclassification of web pages as undesirable categories.

Cited by 2

Blind Multiclass Ensemble Classification

Traganitis, Panagiotis A., Pagès-Zamora, Alba, Giannakis, Georgios B. · arXiv (Cornell University) · 2017

The rising interest in pattern recognition and data analytics has spurred the development of innovative machine learning algorithms and tools. However, as each algorithm has its strengths and limitations, one is motivated to judiciously fuse multiple algorithms in order to find the "best" performing one, for a given dataset. Ensemble learning aims at such high-performance meta-algorithm, by combining the outputs from multiple algorithms. The present work introduces a blind scheme for learning from ensembles of classifiers, using a moment matching method that leverages joint tensor and matrix factorization. Blind refers to the combiner who has no knowledge of the ground-truth labels that each classifier has been trained on. A rigorous performance analysis is derived and the proposed scheme is evaluated on synthetic and real datasets.

Cited by 0Open access

Multiclass Classification Performance Curve

Jesús S. Aguilar–Ruiz, Marcin Michalak · IEEE Access · 2022

Quality of predictive models is a critical factor. Many evaluation measures have been proposed for binary and multi&#x2013;class datasets. However, less attention has been paid to graphical representation of the classification performance, where the ROC curve is extensively used for binary datasets but there is no standard method accepted by the scientific community for multi&#x2013;class datasets. In this work, a multi&#x2013;class classification performance (MCP) curve based on the Hellinger distance between true and prediction probabilities of the classifier is introduced. The MCP curve shows the classification performance, contributes to highlight the low or high confidence on correct predictions, and quantifies the quality by means of the area under the curve.

Cited by 17Open access

Blind Multiclass Ensemble Classification

Panagiotis A. Traganitis, Alba Pagés-Zamora, Georgios B. Giannakis · IEEE Transactions on Signal Processing · 2018

The rising interest in pattern recognition and data analytics has spurred the development of innovative machine learning algorithms and tools. However, as each algorithm has its strengths and limitations, one is motivated to judiciously fuse multiple algorithms in order to find the “best” performing one, for a given dataset. Ensemble learning aims at such high-performance meta-algorithm, by combining the outputs from multiple algorithms. The present work introduces a blind scheme for learning from ensembles of classifiers, using a moment matching method that leverages joint tensor and matrix factorization. Blind refers to the combiner who has no knowledge of the ground-truth labels that each classifier has been trained on. A rigorous performance analysis is derived and the proposed scheme is evaluated on synthetic and real datasets.

Cited by 31Open access

Multiclass Classification for Hawkes Processes

Christophe Denis, Charlotte Dion, Laure Sansonnet · HAL (Le Centre pour la Communication Scientifique Directe) · 2021

We investigate the multiclass classification problem where the features are event sequences. More precisely, the data are assumed to be generated by a mixture of simple linear Hawkes processes. In this new setting, the classes are discriminated by various triggering kernels. A challenge is then to build an efficient classification procedure. We derive the optimal Bayes rule and provide a twostep estimation procedure of the Bayes classifier. In the first step, the weights of the mixture are estimated; in the second step, an empirical risk minimization procedure is performed to estimate the parameters of the Hawkes processes. We establish the consistency of the resulting procedure and derive rates of convergence. Finally, the numerical properties of the data-driven algorithm are illustrated through a simulation study where the triggering kernels are assumed to belong to the popular parametric exponential family. It highlights the accuracy and the robustness of the proposed algorithm. In particular, even if the underlying kernels are misspecified, the procedure exhibits good performance.

Cited by 0Open access

Multiclass classification of dephasing channels

Adriano Macarone Palmieri, Federico Bianchi, Matteo G. A. Paris et al. · Physical review. A/Physical review, A · 2021

We address the use of neural networks (NNs) in classifying the environmental parameters of single-qubit dephasing channels. In particular, we investigate the performance of linear perceptrons and of two nonlinear NN architectures. At variance with time-series-based approaches, our goal is to learn a discretized probability distribution over the parameters using tomographic data at just two random instants of time. We consider dephasing channels originating either from classical $1/{f}^{\ensuremath{\alpha}}$ noise or from the interaction with a bath of quantum oscillators. The parameters to be classified are the color $\ensuremath{\alpha}$ of the classical noise or the Ohmicity parameter $s$ of the quantum environment. In both cases, we find that NNs are able to exactly classify parameters into 16 classes using noiseless data (a linear NN is enough for the color, whereas a single-layer NN is needed for the Ohmicity). In the presence of noisy data (e.g., coming from noisy tomographic measurements), the network is able to classify the color of the $1/{f}^{\ensuremath{\alpha}}$ noise into 16 classes with about $70%$ accuracy, whereas classification of Ohmicity turns out to be challenging. We also consider a more coarse-grained task and train the network to discriminate between two macroclasses corresponding to $\ensuremath{\alpha}\ensuremath{\lessgtr}1$ and $s\ensuremath{\lessgtr}1$, obtaining up to $96%$ and $79%$ accuracy using single-layer NNs.

Cited by 0Open access

Multiclass Event Classification from Text

Daler Ali, Malik Muhammad Saad Missen, Mujtaba Husnain · Scientific Programming · 2021

Social media has become one of the most popular sources of information. People communicate with each other and share their ideas, commenting on global issues and events in a multilingual environment. While social media has been popular for several years, recently, it has given an exponential rise in online data volumes because of the increasing popularity of local languages on the web. This allows researchers of the NLP community to exploit the richness of different languages while overcoming the challenges posed by these languages. Urdu is also one of the most used local languages being used on social media. In this paper, we presented the first-ever event detection approach for Urdu language text. Multiclass event classification is performed by popular deep learning (DL) models, i.e.,Convolution Neural Network (CNN), Recurrence Neural Network (RNN), and Deep Neural Network (DNN). The one-hot-encoding, word embedding, and term-frequency inverse document frequency- (TF-IDF-) based feature vectors are used to evaluate the Deep Learning(DL) models. The dataset that is used for experimental work consists of more than 0.15 million (103965) labeled sentences. DNN classifier has achieved a promising accuracy of 84% in extracting and classifying the events in the Urdu language script.

Cited by 31Open access

Multiclass Classification on 3D Shapes

Honghua Li · 2016

I present a multiclass classification method for 3D shapes in this project. First, D2 shape distribution is used to generate discriminative representation of 3D shapes, which is developed to be invariant to rigid transformations and scalings. Next I employ the all-versus-all approach to decompose the multiclass classification problem into several binary classification problems, which are solved later by Support Vector Machine. The experiments performed show that the proposed parameter-free method is successful on a large testing dataset. 1

Cited by 0