Binary Classification Datasets Research Articles

An Interpretable Deep-Stacked TSK Fuzzy Classifier for High-Dimensional Problems

This study proposes a novel deep-stacked fully interpretable and more generalization Takagi-Sugeno-Kang fuzzy system (D-FIMG-TSK) to obtain short fuzzy rules with high interpretability in each layer and simultaneously earn more generalization capability for high-dimensional data classification tasks. With the help of both discriminative and residual information, D-FIMG-TSK combines several fully interpretable TSK classifier FIMG-TSK in a stacked manner to conveniently move apart the manifolds existing in the original data space to achieve better linear separability. Besides, a feature selection method is designed to make feature selection from original feature sets on all layers to guarantee the classification performances. On the training process of D-FIMG-TSK, the importance of all original features and the antecedent and consequent parts of all fuzzy rules in each FIMG-TSK can be determined at the same time. The effectiveness of D-FIMG-TSK is manifested by the experimental results on eight binary high-dimensional classification datasets.

Corrosion image classification method based on EfficientNetV2

Corrosion is one of the key factors leading to material failure, which can occur in facilities and equipment closely related to people's lives, causing structural damage and thus affecting the safety of people's lives and property. To identify corrosion more effectively across multiple facilities and equipment, this paper utilizes a corrosion binary classification dataset containing various materials to develop a CNN classification model for better detection and distinction of material corrosion, using a methodological paradigm of transfer learning and fine-tuning. The proposed model implementation initially uses data augmentation to enhance the dataset and employs different sizes of EfficientNetV2 for training, evaluated using Confusion Matrix, ROC curve, and the values of Precision, Recall, and F1-score. To further enhance the testing results, this paper focuses on the impact of using the Global Average Pooling layer versus the Global Max Pooling layer, as well as the number of fine-tuning layers. The results show that the Global Average Pooling layer performs better, and EfficientNetV2B0 with a fine-tuning rate of 20 %, and EfficientNetV2S with a fine-tuning rate of 15 %, achieve the highest testing accuracy of 0.9176, an ROC-AUC value of 0.97, and Precision, Recall, and F1-Score values exceeding 0.9. These findings can be served as a reference for other corrosion classification models which uses EfficientNetV2.

Fine-grained sentiment analysis using multidimensional feature fusion and GCN

ABSTRACT The demand for analysing sentiment information in social media data is increasing. However, current fine-grained sentiment analysis methods fail to consider both global and local semantic features simultaneously, leading to the oversight of grammatical information in sentences and an inability to address the issue of polysemy. To address these challenges, we propose a microblog fine-grained sentiment analysis model based on multidimensional feature fusion and graph convolutional neural networks (GCN). Built upon the ALBERT model, we utilize BiLSTM and capsule network models to extract global and local semantic features, thereby capturing bidirectional semantic dependencies and textual positional semantic information. Finally, we employ multi-head self-attention and GCN to select key features and sentence information, ensuring the integrity of fine-grained features. The experimental results indicate that the model outperforms several other models on fine-grained sentiment analysis datasets SMP2020-EWECT, NLPCC2013, NLPCC2014, and the binary classification dataset weibo_senti_100k, achieving accuracies of 80.64%, 67.19%, 71.37%, and 98.43%, respectively.

An Advanced Methodology for Crystal System Detection in Li-ion Batteries

Detecting the crystal system of lithium-ion batteries is crucial for optimizing their performance and safety. Understanding the arrangement of atoms or ions within the battery’s electrodes and electrolyte allows for improvements in energy density, cycling stability, and safety features. This knowledge also guides material design and fabrication techniques, driving advancements in battery technology for various applications. In this paper, a publicly available dataset was utilized to develop mathematical equations (MEs) using a genetic programming symbolic classifier (GPSC) to determine the type of crystal structure in Li-ion batteries with a high classification performance. The dataset consists of three different classes transformed into three binary classification datasets using a one-versus-rest approach. Since the target variable of each dataset variation is imbalanced, several oversampling techniques were employed to achieve balanced dataset variations. The GPSC was trained on these balanced dataset variations using a five-fold cross-validation (5FCV) process, and the optimal GPSC hyperparameter values were searched for using a random hyperparameter value search (RHVS) method. The goal was to find the optimal combination of GPSC hyperparameter values to achieve the highest classification performance. After obtaining MEs using the GPSC with the highest classification performance, they were combined and tested on initial binary classification dataset variations. Based on the conducted investigation, the ensemble of MEs could detect the crystal system of Li-ion batteries with a high classification accuracy (1.0).

A data augmentation approach that ensures the reliability of foregrounds in medical image segmentation

Medical image segmentation is an important task in medical imaging and diagnosis. Data augmentation can substantially improve the accuracy of medical image segmentation when the dataset has a small amount of medical images. However, the data augmentation methods for medical image are usually based on big models that require extensive search space. Furthermore, excessively complex models often have a heavy burden for the general healthcare organization or researcher. To address this problem, we propose a method of data augmentation that is simple to implement even for the general researcher and simple to transplant across various models. Here we introduce our new methods called KeepMask and KeepMix, which can be simply ported to a variety of models and provide high performance. These methods allow data augmentation without any effect on the target organ or lesion and can also be adapted to multi-class segmentation. KeepMask and KeepMix can not only perturb the background of an existing medical image but also add target organs that are not present to it and generate new images based on the image. In this paper, we performed our methods on both binary class datasets and multi-class datasets and obtained better performance. We conducted numerous experiments showing the predicted segmentation images using our proposed methods obtained more accurate boundaries.

Effective text classification using BERT, MTM LSTM, and DT

Text classification plays a critical role in managing large volumes of electronically produced texts. As the number of such texts increases, manual analysis becomes impractical, necessitating an intelligent approach for processing information. Deep learning models have witnessed widespread application in text classification, including the use of recurrent neural networks like Many to One Long Short-Term Memory (MTO LSTM). Nonetheless, this model is limited by its reliance on only the last token for text labelling. To overcome this limitation, this study introduces a novel hybrid model that combines Bidirectional Encoder Representations from Transformers (BERT), Many To Many Long Short-Term Memory (MTM LSTM), and Decision Templates (DT) for text classification. In this new model, the text is first embedded using the BERT model and then trained using MTM LSTM to approximate the target at each token. Finally, the approximations are fused using DT. The proposed model is evaluated using the well-known IMDB movie review dataset for binary classification and Drug Review Dataset for multiclass classification. The results demonstrate superior performance in terms of accuracy, recall, precision, and F1 score compared to previous models. The hybrid model presented in this study holds significant potential for a wide range of text classification tasks and stands as a valuable contribution to the field.

An Innovative Novel Transformer Model and Datasets for Safeguarding Religious Sensitivities in Online Social Platforms

Nowadays, blasphemy, inter-religious hatred, and intolerance toward other people's faith become increasing problems in different social media. To effectively tackle these problems, it is necessary to develop efficient techniques for automated detection. With the increasing concern about hate speech on the internet, this research study focuses on creating and testing advanced models to identify and categorize texts related to religious hatred. Leveraging two meticulously curated datasets, the first comprising binary classification tasks to discern hate speech directed towards religion and the second featuring a nuanced five-class categorization including Blasphemy, Ethnic Hatred, Ethnic Group Hatred, Undetermined, and Non-Hatred. The main focus of the research is examining the effectiveness of different established machine learning techniques, deep learning methods, and pre-trained transformer models. Notably, the study introduces hatebnBERT, a novel BERT-based model tailored to the Bangla language's specific nuances and linguistic intricacies specifically built for hate speech detection. Through rigorous evaluation and comparative analysis, the results demonstrate the superior accuracy and precision of hatebnBERT, achieving an accuracy of 98.8% in the binary-class dataset and 98.6% in the multiclass dataset, in effectively identifying and classifying various forms of hate speech within the realm of religious discourse. The outcomes underscore the significance of leveraging specialized language models, such as hatebnBERT, to combat the proliferation of abusive speech and promote a more courteous and inclusive online social media environment within the Bangla-speaking community, considering the potentially devastating consequences of unchecked religious hatred propagation. Insert here your abstract text.

Design of a photonic unitary neural network based on MZI arrays

In recent years, optical neural networks have attracted widespread attention, due to their advantages of high speed, high parallelism, high bandwidth, and low power consumption. Photonic unitary neural network is a kind of neural networks that utilize the principles of unitary matrices and photonics to perform computations. In this paper, we design a photonic unitary neural network based on Mach–Zehnder interferometer arrays. The results show that the network has a good performance on both triangular and circular binary classification datasets, where most of the data points are correctly classified. The accuracies achieve 97% and 95% for triangular and circular datasets, with the loss function values of 0.023 and 0.046, respectively.

Pellet image segmentation model of superpixel feature-based support vector machine in digital twin

A digital twin model based on superpixel features is established to solve the problem of noise and similar gray values between foreground and background of pellet images. With superpixel as the basic unit of segmentation, the influence of single pixel on segmentation results is reduced, and allows for higher segmentation accuracy. The gray-level co-occurrence matrix is used to represent the superpixel characteristic information, and the color moment and gray level distribution are combined to comprehensively characterize the superpixel. Through principal component analysis and correlation analysis, The feature compression of superpixel is realized, and the computational efficiency is improved. The superpixel binary classification data set is built, and the multi-dimensional feature information of superpixel is extracted as input vector to train the binary classification model of SVM, and the image segmentation problem is transformed into foreground and background classification problem. A multi-scale superpixel segmentation boundary optimization method is proposed to further refine the boundary region of foreground and background. A four-neighborhood search algorithm is proposed to reduce the missegmentation rate of edge superpixels. Experimental results show that the accuracy of the proposed method can reach 95.87%, the precision of image edge segmentation is high, and the foreground and background of granular image are accurately segmented. The digital twin model is established which can provide basis for the subsequent visual analysis and decision.

Identifying dyslexia in school pupils from eye movement and demographic data using artificial intelligence.

This paper represents our research results in the pursuit of the following objectives: (i) to introduce a novel multi-sources data set to tackle the shortcomings of the previous data sets, (ii) to propose a robust artificial intelligence-based solution to identify dyslexia in primary school pupils, (iii) to investigate our psycholinguistic knowledge by studying the importance of the features in identifying dyslexia by our best AI model. In order to achieve the first objective, we collected and annotated a new set of eye-movement-during-reading data. Furthermore, we collected demographic data, including the measure of non-verbal intelligence, to form our three data sources. Our data set is the largest eye-movement data set globally. Unlike the previously introduced binary-class data sets, it contains (A) three class labels and (B) reading speed. Concerning the second objective, we formulated the task of dyslexia prediction as regression and classification problems and scrutinized the performance of 12 classifications and eight regressions approaches. We exploited the Bayesian optimization method to fine-tune the hyperparameters of the models: and reported the average and the standard deviation of our evaluation metrics in a stratified ten-fold cross-validation. Our studies showed that multi-layer perceptron, random forest, gradient boosting, and k-nearest neighbor form the group having the most acceptable results. Moreover, we showed that although separately using each data source did not lead to accurate results, their combination led to a reliable solution. We also determined the importance of the features of our best classifier: our findings showed that the IQ, gender, and age are the top three important features; we also showed that fixation along the y-axis is more important than other fixation data. Dyslexia detection, eye fixation, eye movement, demographic, classification, regression, artificial intelligence.

RAU: Novel Activation Function for Deep Learning Neural Network

In Deep learning neural networks (DNNs) activation functions perform a vital role. In each neuron activation function is responsible for generating output signals from given input signals. Hence, activation function is one of the factors that influence the performance of DNN. A novel activation unit RAU (Reciprocal activation unit) is proposed in this paper. Most of the popular algorithms given more importance to positive signals, but proposed method handles the negative and positive inputs equally. The proposed RAU tested with both multiclassification and binary classification datasets. Iris flower and Wisconsin Breast Cancer datasets are used for the analysis. In Breast cancer dataset RAU provides 99.25% and 97.08% accuracy for classification of train and test sets respectively. In Iris dataset RAU provides 99.05% and 97.78% accuracy for the classification of train and test sets. Analysis of the same datasets are performed with the existing activation functions- Sigmoid, RMAF, Swish, Tanh and ReLU. Results showed that RAU performed better than other activation functions.

Comparing Different Oversampling Methods in Predicting Multi-Class Educational Datasets Using Machine Learning Techniques

Abstract Predicting students’ academic performance is a critical research area, yet imbalanced educational datasets, characterized by unequal academic-level representation, present challenges for classifiers. While prior research has addressed the imbalance in binary-class datasets, this study focuses on multi-class datasets. A comparison of ten resampling methods (SMOTE, Adasyn, Distance SMOTE, BorderLineSMOTE, KmeansSMOTE, SVMSMOTE, LN SMOTE, MWSMOTE, Safe Level SMOTE, and SMOTETomek) is conducted alongside nine classification models: K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Support Vector Machine (SVM), Logistic Regression (LR), Extra Tree (ET), Random Forest (RT), Extreme Gradient Boosting (XGB), and Ada Boost (AdaB). Following a rigorous evaluation, including hyperparameter tuning and 10 fold cross-validations, KNN with SmoteTomek attains the highest accuracy of 83.7%, as demonstrated through an ablation study. These results emphasize SMOTETomek’s effectiveness in mitigating class imbalance in educational datasets and highlight KNN’s potential as an educational data mining classifier.

Extreme Gradient Boosting Algorithm to Improve Machine Learning Model Performance on Multiclass Imbalanced Dataset

Unbalanced conditions in the dataset often become a real-world problem, especially in machine learning. Class imbalance in the dataset is a condition where the number of minority classes is much smaller than the majority class, or the number is insufficient. Machine learning models tend to recognize patterns in the majority class more than in the minority class. This problem is one of the most critical challenges in machine learning research, so several methods have been developed to overcome it. However, most of these methods only focus on binary datasets, so few methods still focus on multiclass datasets. Handling unbalanced multiclass is more complex than handling unbalanced binary because it involves more classes than binary class datasets. With these problems, we need an algorithm with features that can support adjustments to the difficulties that arise in multiclass unbalanced datasets. One of the algorithms that have features for adjustment is the ensemble algorithm, namely Xtreme Gradient Boosting. Based on the research, our proposed method with Xtreme Gradient Boosting showed better results than the other classification and ensemble algorithms on eight datasets with five evaluation metrics indicators such as balanced accuracy, the geometric-mean, multiclass area under the curve, true positive rate, and true negative rate. In future research, we suggest combining methods at the data level and Xtreme Gradient Boosting. With the performance increase in Xtreme Gradient Boosting, it can be a solution and reference in the case of handling multiclass imbalanced problems. Besides, we also recommended testing with datasets in the form of categorical and continuous data.

Explainable patch-level histopathology tissue type detection with bag-of-local-features models and data augmentation

Abstract Automatic detection of tissue types on whole-slide images (WSI) is an important task in computational histopathology that can be solved with convolutional neural networks (CNN) with high accuracy. However, the black-box nature of CNNs rightfully raises concerns about using them for this task. In this paper, we reformulate the task of tissue type detection to multiple binary classification problems to simplify the justification of model decisions. We propose an adapted Bag-of-local-Features interpretable CNN for solving this problem, which we train on eight newly introduced binary tissue classification datasets. The performance of the model is evaluated simultaneously with its decision-making process using logit heatmaps. Our model achieves better performance than its non-interpretable counterparts, while also being able to provide human-readable justification for decisions. Furthermore, the problem of data scarcity in computational histopathology is accounted for by using data augmentation techniques to improve both the performance and even the validity of model decisions. The source code and binary datasets can be accessed at: https://github.com/galigergergo/BolFTissueDetect.

An Efficient Ensemble Approach for Alzheimer's Disease Detection Using an Adaptive Synthetic Technique and Deep Learning.

Alzheimer's disease is an incurable neurological disorder that leads to a gradual decline in cognitive abilities, but early detection can significantly mitigate symptoms. The automatic diagnosis of Alzheimer's disease is more important due to the shortage of expert medical staff, because it reduces the burden on medical staff and enhances the results of diagnosis. A detailed analysis of specific brain disorder tissues is required to accurately diagnose the disease via segmented magnetic resonance imaging (MRI). Several studies have used the traditional machine-learning approaches to diagnose the disease from MRI, but manual extracted features are more complex, time-consuming, and require a huge amount of involvement from expert medical staff. The traditional approach does not provide an accurate diagnosis. Deep learning has automatic extraction features and optimizes the training process. The Magnetic Resonance Imaging (MRI) Alzheimer's disease dataset consists of four classes: mild demented (896 images), moderate demented (64 images), non-demented (3200 images), and very mild demented (2240 images). The dataset is highly imbalanced. Therefore, we used the adaptive synthetic oversampling technique to address this issue. After applying this technique, the dataset was balanced. The ensemble of VGG16 and EfficientNet was used to detect Alzheimer's disease on both imbalanced and balanced datasets to validate the performance of the models. The proposed method combined the predictions of multiple models to make an ensemble model that learned complex and nuanced patterns from the data. The input and output of both models were concatenated to make an ensemble model and then added to other layers to make a more robust model. In this study, we proposed an ensemble of EfficientNet-B2 and VGG-16 to diagnose the disease at an early stage with the highest accuracy. Experiments were performed on two publicly available datasets. The experimental results showed that the proposed method achieved 97.35% accuracy and 99.64% AUC for multiclass datasets and 97.09% accuracy and 99.59% AUC for binary-class datasets. We evaluated that the proposed method was extremely efficient and provided superior performance on both datasets as compared to previous methods.

An algorithm for training a class of polynomial models

In this paper we propose a new training algorithm for a class of polynomial models. The algorithm is derived using a learning bound for predictors that are convex combinations of functions from simpler classes. In our case, the hypotheses are polynomials over the input features, and they are interpreted as convex combinations of homogeneous polynomials. In addition, the coefficients are restricted to be positive and to sum to 1. This constraint will simplify the interpretation of the model. The training is done by minimizing a surrogate of the learning bound, using an iterative two-phase algorithm. Basically, in the first phase the algorithm decides which monomials of higher degree should be added, and in the second phase the coefficients are recomputed by solving a convex program. We performed several experiments on binary classification datasets from different domains. Experiments show that the algorithm compares favorably in terms of accuracy and speed with other classification methods, including some new interpretable methods like Neural Additive Models and CORELS. In addition, the resulting predictor can sometimes be understood and validated by a domain expert. The code is publicly available.

Cyberbullying Detection

The harmful effects of cyberbullying, including mental health problems, poor academic performance, and suicidal thoughts, highlight the importance of developing effective detection systems. This text discusses various approaches to detecting and combating cyberbullying on social media platforms. A cyberbullying detection system (CDS) could identify different types of bullying based on gender, religion, ethnicity, age, aggression, and non-cyberbullying. The CDS system utilized a hybrid deep learning architecture that integrated convolutional neural networks (CNN) with bidirectional long short-term memory networks (BiLSTM). Both binary and multiclass classification datasets were used, and the BiLSTM outperformed the combined CNN-BiLSTM classifier in detecting online bullying with an accuracy rate of 99%. A novel algorithm called CNN-CB was proposed to eliminate the need for feature engineering and to produce better predictions than traditional cyberbullying detection approaches. CNN-CB utilizes convolutional neural networks and incorporates semantics through the use of word embedding. Experiments showed that CNN-CB outperformed traditional content-based cyberbullying detection with an accuracy of 95%.

Classification of breast tumors by using a novel approach based on deep learning methods and feature selection

Cancer is one of the most insidious diseases that the most important factor in overcoming the cancer is early diagnosis and detection. The histo-pathological images are used to determine whether the tissue is cancerous and the type of cancer. As the result of examination on tissue images by the expert personnel, the cancer type, and stage of the tissue can be determined. However, this situation can cause both time and energy loss as well as personnel-related inspection errors. By the increased usage of computer-based decision methods in the last decades, it would be more efficient and accurate to detect and classify the cancerous tissues with computer-aided systems. As classical image processing methods were used for cancer-type detection in early studies, advanced deep learning methods based on recurrent neural networks and convolutional neural networks have been used more recently. In this paper, popular deep learning methods such as ResNet-50, GoogLeNet, InceptionV3, and MobilNetV2 are employed by implementing novel feature selection method in order to classify cancer type on a local binary class dataset and multi-class BACH dataset. The classification performance of the proposed feature selection implemented deep learning methods follows as for the local binary class dataset 98.89% and 92.17% for BACH dataset which is much better than most of the obtained results in literature. The obtained findings on both datasets indicates that the proposed methods can detect and classify the cancerous type of a tissue with high accuracy and efficiency.

Peripheral blood mononuclear cell derived biomarker detection using eXplainable Artificial Intelligence (XAI) provides better diagnosis of breast cancer

The incidence and mortality rate of breast cancer increases yearly by an average of 1.44 % and 0.23 %, respectively. Till 2021, there were 7.8 million women who had been diagnosed with breast cancer within 5 years. Biopsies of tumors are often expensive and invasive and raise the risk of serious complications like infection, excessive bleeding, and puncture damage to nearby tissues and organs. Early detection biomarkers are often variably expressed in different patients and may even be below the detection level at an early stage. Hence PBMC that shows alteration in gene profile as a result of interaction with tumor antigens may serve as a better early detection biomarker. Also, such alterations in immune gene profile in PBMCs are more prone to detection despite variability in different breast cancer mutants.This study aimed to identify potential diagnostic biomarkers for breast cancer using eXplainable Artificial Intelligence (XAI) on XGBoost machine learning (ML) models trained on a binary classification dataset containing the expression data of PBMCs from 252 breast cancer patients and 194 healthy women.After effectively adding SHAP values further into the XGBoost model, ten important genes related to breast cancer development were discovered to be effective potential biomarkers. Our studies showed that SVIP, BEND3, MDGA2, LEF1-AS1, PRM1, TEX14, MZB1, TMIGD2, KIT, and FKBP7 are key genes that impact model prediction. These genes may serve as early, non-invasive diagnostic and prognostic biomarkers for breast cancer patients.

Non-stationary neural signal to image conversion framework for image-based deep learning algorithms.

This paper presents a time-efficient preprocessing framework that converts any given 1D physiological signal recordings into a 2D image representation for training image-based deep learning models. The non-stationary signal is rasterized into the 2D image using Bresenham's line algorithm with time complexity O(n). The robustness of the proposed approach is evaluated based on two publicly available datasets. This study classified three different neural spikes (multi-class) and EEG epileptic seizure and non-seizure (binary class) based on shapes using a modified 2D Convolution Neural Network (2D CNN). The multi-class dataset consists of artificially simulated neural recordings with different Signal-to-Noise Ratios (SNR). The 2D CNN architecture showed significant performance for all individual SNRs scores with (SNR/ACC): 0.5/99.69, 0.75/99.69, 1.0/99.49, 1.25/98.85, 1.5/97.43, 1.75/95.20 and 2.0/91.98. Additionally, the binary class dataset also achieved 97.52% accuracy by outperforming several others proposed algorithms. Likewise, this approach could be employed on other biomedical signals such as Electrocardiograph (EKG) and Electromyography (EMG).