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"Classifiers"
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Least Ambiguous Set-Valued Classifiers With Bounded Error Levels
In most classification tasks, there are observations that are ambiguous and therefore difficult to correctly label. Set-valued classifiers output sets of plausible labels rather than a single label, thereby giving a more appropriate and informative treatment to the labeling of ambiguous instances. We introduce a framework for multiclass set-valued classification, where the classifiers guarantee user-defined levels of coverage or confidence (the probability that the true label is contained in the set) while minimizing the ambiguity (the expected size of the output). We first derive oracle classifiers assuming the true distribution to be known. We show that the oracle classifiers are obtained from level sets of the functions that define the conditional probability of each class. Then we develop estimators with good asymptotic and finite sample properties. The proposed estimators build on existing single-label classifiers. The optimal classifier can sometimes output the empty set, but we provide two solutions to fix this issue that are suitable for various practical needs. Supplementary materials for this article are available online.
Journal Article
An All-Inclusive Machine Learning and Deep Learning Method for Forecasting Cardiovascular Disease in Bangladeshi Population
INTRODUCTION: Cardiovascular disease is a major concern and pressing issue faced by the healthcare sector globally. According to a survey conducted by the WHO every year, CVDs cause 17.9 million deaths worldwide. Lack of pre-prediction of CVDs is a significant factor contributing to the death of patients. Predicting CVDs is a challenging task for medical practitioners as it requires a high level of medical analysis skills and extensive knowledge. OBJECTIVES: We believe that the improvement in the accuracy of prediction can significantly reduce the risk caused by CVDs and help medical practitioners better diagnose patients . METHODS: In this study, We created a CVD prediction model. using a ML approach. We utilized various algorithms, including logistic regression, Gaussian Naive Baye, Bernoulli Naive Baye, SVM, KNN, optimized KNN, X Gradient Boosting, and random forest algorithms to analyze and predict CVDs. RESULTS: Our developed prediction model achieved an accuracy of 96.7%, indicating its effectiveness in predicting CVDs. DL algorithms can also assist in identifying, classifying, and quantifying patterns of medical images, improving patient evaluation and diagnosis based on prior medical history and evaluation patterns. CONCLUSION: Furthermore, deep learning algorithms can help in developing new drugs with minimum cost by reducing the number of clinical research trials, using prior prediction of the drug's efficacy.
Journal Article
Nearest neighbors distance ratio open-set classifier
In this paper, we propose a novel multiclass classifier for the open-set recognition scenario. This scenario is the one in which there are no a priori training samples for some classes that might appear during testing. Usually, many applications are inherently open set. Consequently, successful closed-set solutions in the literature are not always suitable for real-world recognition problems. The proposed open-set classifier extends upon the Nearest-Neighbor (NN) classifier. Nearest neighbors are simple, parameter independent, multiclass, and widely used for closed-set problems. The proposed Open-Set NN (OSNN) method incorporates the ability of recognizing samples belonging to classes that are unknown at training time, being suitable for open-set recognition. In addition, we explore evaluation measures for open-set problems, properly measuring the resilience of methods to unknown classes during testing. For validation, we consider large freely-available benchmarks with different open-set recognition regimes and demonstrate that the proposed OSNN significantly outperforms their counterparts in the literature.
Journal Article
The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances
Time Series Classification (TSC) involves building predictive models for a discrete target variable from ordered, real valued, attributes. Over recent years, a new set of TSC algorithms have been developed which have made significant improvement over the previous state of the art. The main focus has been on univariate TSC, i.e. the problem where each case has a single series and a class label. In reality, it is more common to encounter multivariate TSC (MTSC) problems where the time series for a single case has multiple dimensions. Despite this, much less consideration has been given to MTSC than the univariate case. The UCR archive has provided a valuable resource for univariate TSC, and the lack of a standard set of test problems may explain why there has been less focus on MTSC. The UEA archive of 30 MTSC problems released in 2018 has made comparison of algorithms easier. We review recently proposed bespoke MTSC algorithms based on deep learning, shapelets and bag of words approaches. If an algorithm cannot naturally handle multivariate data, the simplest approach to adapt a univariate classifier to MTSC is to ensemble it over the multivariate dimensions. We compare the bespoke algorithms to these dimension independent approaches on the 26 of the 30 MTSC archive problems where the data are all of equal length. We demonstrate that four classifiers are significantly more accurate than the benchmark dynamic time warping algorithm and that one of these recently proposed classifiers, ROCKET, achieves significant improvement on the archive datasets in at least an order of magnitude less time than the other three.
Journal Article
Two-Stage Hybrid Data Classifiers Based on SVM and kNN Algorithms
The paper considers a solution to the problem of developing two-stage hybrid SVM-kNN classifiers with the aim to increase the data classification quality by refining the classification decisions near the class boundary defined by the SVM classifier. In the first stage, the SVM classifier with default parameters values is developed. Here, the training dataset is designed on the basis of the initial dataset. When developing the SVM classifier, a binary SVM algorithm or one-class SVM algorithm is used. Based on the results of the training of the SVM classifier, two variants of the training dataset are formed for the development of the kNN classifier: a variant that uses all objects from the original training dataset located inside the strip dividing the classes, and a variant that uses only those objects from the initial training dataset that are located inside the area containing all misclassified objects from the class dividing strip. In the second stage, the kNN classifier is developed using the new training dataset above-mentioned. The values of the parameters of the kNN classifier are determined during training to maximize the data classification quality. The data classification quality using the two-stage hybrid SVM-kNN classifier was assessed using various indicators on the test dataset. In the case of the improvement of the quality of classification near the class boundary defined by the SVM classifier using the kNN classifier, the two-stage hybrid SVM-kNN classifier is recommended for further use. The experimental results approve the feasibility of using two-stage hybrid SVM-kNN classifiers in the data classification problem. The experimental results obtained with the application of various datasets confirm the feasibility of using two-stage hybrid SVM-kNN classifiers in the data classification problem.
Journal Article
Dementia classification using MR imaging and clinical data with voting based machine learning models
Dementia is one of the leading causes of severe cognitive decline, it induces memory loss and impairs the daily life of millions of people worldwide. In this work, we consider the classification of dementia using magnetic resonance (MR) imaging and clinical data with machine learning models. We adapt univariate feature selection in the MR data pre-processing step as a filter-based feature selection. Bagged decision trees are also implemented to estimate the important features for achieving good classification accuracy. Several ensemble learning-based machine learning approaches, namely gradient boosting (GB), extreme gradient boost (XGB), voting-based, and random forest (RF) classifiers, are considered for the diagnosis of dementia. Moreover, we propose voting-based classifiers that train on an ensemble of numerous basic machine learning models, such as the extra trees classifier, RF, GB, and XGB. The implementation of a voting-based approach is one of the important contributions, and the performance of different classifiers are evaluated in terms of precision, accuracy, recall, and F1 score. Moreover, the receiver operating characteristic curve (ROC) and area under the ROC curve (AUC) are used as metrics for comparing these classifiers. Experimental results show that the voting-based classifiers often perform better compared to the RF, GB, and XGB in terms of precision, recall, and accuracy, thereby indicating the promise of differentiating dementia from imaging and clinical data.
Journal Article
Arabic text classification: the need for multi-labeling systems
The process of tagging a given text or document with suitable labels is known as text categorization or classification. The aim of this work is to automatically tag a news article based on its vocabulary features. To accomplish this objective, 2 large datasets have been constructed from various Arabic news portals. The first dataset contains of 90k single-labeled articles from 4 domains (Business, Middle East, Technology and Sports). The second dataset has over 290 k multi-tagged articles. To examine the single-label dataset, we employed an array of ten shallow learning classifiers. Furthermore, we added an ensemble model that adopts the majority-voting technique of all studied classifiers. The performance of the classifiers on the first dataset ranged between 87.7% (AdaBoost) and 97.9% (SVM). Analyzing some of the misclassified articles confirmed the need for a multi-label opposed to single-label categorization for better classification results. For the second dataset, we tested both shallow learning and deep learning multi-labeling approaches. A custom accuracy metric, designed for the multi-labeling task, has been developed for performance evaluation along with hamming loss metric. Firstly, we used classifiers that were compatible with multi-labeling tasks such as Logistic Regression and XGBoost, by wrapping each in a OneVsRest classifier. XGBoost gave the higher accuracy, scoring 84.7%, while Logistic Regression scored 81.3%. Secondly, ten neural networks were constructed (CNN, CLSTM, LSTM, BILSTM, GRU, CGRU, BIGRU, HANGRU, CRF-BILSTM and HANLSTM). CGRU proved to be the best multi-labeling classifier scoring an accuracy of 94.85%, higher than the rest of the classifies.
Journal Article
Leveraging Machine Learning for Fraudulent Social Media Profile Detection
Fake social media profiles are responsible for various cyber-attacks, spreading fake news, identity theft, business and payment fraud, abuse, and more. This paper aims to explore the potential of Machine Learning in detecting fake social media profiles by employing various Machine Learning algorithms, including the Dummy Classifier, Support Vector Classifier (SVC), Support Vector Classifier (SVC) kernels, Random Forest classifier, Random Forest Regressor, Decision Tree Classifier, Decision Tree Regressor, MultiLayer Perceptron classifier (MLP), MultiLayer Perceptron (MLP) Regressor, Naïve Bayes classifier, and Logistic Regression. For a comprehensive evaluation of the performance and accuracy of different models in detecting fake social media profiles, it is essential to consider confusion matrices, sampling techniques, and various metric calculations. Additionally, incorporating extended computations such as root mean squared error, mean absolute error, mean squared error and cross-validation accuracy can further enhance the overall performance of the models.
Journal Article
A review of epileptic seizure detection using machine learning classifiers
Epilepsy is a serious chronic neurological disorder, can be detected by analyzing the brain signals produced by brain neurons. Neurons are connected to each other in a complex way to communicate with human organs and generate signals. The monitoring of these brain signals is commonly done using Electroencephalogram (EEG) and Electrocorticography (ECoG) media. These signals are complex, noisy, non-linear, non-stationary and produce a high volume of data. Hence, the detection of seizures and discovery of the brain-related knowledge is a challenging task. Machine learning classifiers are able to classify EEG data and detect seizures along with revealing relevant sensible patterns without compromising performance. As such, various researchers have developed number of approaches to seizure detection using machine learning classifiers and statistical features. The main challenges are selecting appropriate classifiers and features. The aim of this paper is to present an overview of the wide varieties of these techniques over the last few years based on the taxonomy of statistical features and machine learning classifiers—‘black-box’ and ‘non-black-box’. The presented state-of-the-art methods and ideas will give a detailed understanding about seizure detection and classification, and research directions in the future.
Journal Article