544 research outputs found

    Support matrix machine: A review

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    Support vector machine (SVM) is one of the most studied paradigms in the realm of machine learning for classification and regression problems. It relies on vectorized input data. However, a significant portion of the real-world data exists in matrix format, which is given as input to SVM by reshaping the matrices into vectors. The process of reshaping disrupts the spatial correlations inherent in the matrix data. Also, converting matrices into vectors results in input data with a high dimensionality, which introduces significant computational complexity. To overcome these issues in classifying matrix input data, support matrix machine (SMM) is proposed. It represents one of the emerging methodologies tailored for handling matrix input data. The SMM method preserves the structural information of the matrix data by using the spectral elastic net property which is a combination of the nuclear norm and Frobenius norm. This article provides the first in-depth analysis of the development of the SMM model, which can be used as a thorough summary by both novices and experts. We discuss numerous SMM variants, such as robust, sparse, class imbalance, and multi-class classification models. We also analyze the applications of the SMM model and conclude the article by outlining potential future research avenues and possibilities that may motivate academics to advance the SMM algorithm

    Three-way Imbalanced Learning based on Fuzzy Twin SVM

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    Three-way decision (3WD) is a powerful tool for granular computing to deal with uncertain data, commonly used in information systems, decision-making, and medical care. Three-way decision gets much research in traditional rough set models. However, three-way decision is rarely combined with the currently popular field of machine learning to expand its research. In this paper, three-way decision is connected with SVM, a standard binary classification model in machine learning, for solving imbalanced classification problems that SVM needs to improve. A new three-way fuzzy membership function and a new fuzzy twin support vector machine with three-way membership (TWFTSVM) are proposed. The new three-way fuzzy membership function is defined to increase the certainty of uncertain data in both input space and feature space, which assigns higher fuzzy membership to minority samples compared with majority samples. To evaluate the effectiveness of the proposed model, comparative experiments are designed for forty-seven different datasets with varying imbalance ratios. In addition, datasets with different imbalance ratios are derived from the same dataset to further assess the proposed model's performance. The results show that the proposed model significantly outperforms other traditional SVM-based methods

    Class-Imbalanced Complementary-Label Learning via Weighted Loss

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    Complementary-label learning (CLL) is widely used in weakly supervised classification, but it faces a significant challenge in real-world datasets when confronted with class-imbalanced training samples. In such scenarios, the number of samples in one class is considerably lower than in other classes, which consequently leads to a decline in the accuracy of predictions. Unfortunately, existing CLL approaches have not investigate this problem. To alleviate this challenge, we propose a novel problem setting that enables learning from class-imbalanced complementary labels for multi-class classification. To tackle this problem, we propose a novel CLL approach called Weighted Complementary-Label Learning (WCLL). The proposed method models a weighted empirical risk minimization loss by utilizing the class-imbalanced complementary labels, which is also applicable to multi-class imbalanced training samples. Furthermore, we derive an estimation error bound to provide theoretical assurance. To evaluate our approach, we conduct extensive experiments on several widely-used benchmark datasets and a real-world dataset, and compare our method with existing state-of-the-art methods. The proposed approach shows significant improvement in these datasets, even in the case of multiple class-imbalanced scenarios. Notably, the proposed method not only utilizes complementary labels to train a classifier but also solves the problem of class imbalance.Comment: 9 pages, 9 figures, 3 table

    Improved adaptive semi-unsupervised weighted oversampling (IA-SUWO) using sparsity factor for imbalanced datasets

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    The imbalanced data problem is common in data mining nowadays due to the skewed nature of data, which impact the classification process negatively in machine learning. For preprocessing, oversampling techniques significantly benefitted the imbalanced domain, in which artificial data is generated in minority class to enhance the number of samples and balance the distribution of samples in both classes. However, existing oversampling techniques encounter through overfitting and over-generalization problems which lessen the classifier performance. Although many clustering based oversampling techniques significantly overcome these problems but most of these techniques are not able to produce the appropriate number of synthetic samples in minority clusters. This study proposed an improved Adaptive Semi-unsupervised Weighted Oversampling (IA-SUWO) technique, using the sparsity factor which determine the sparse minority samples in each minority cluster. This technique consider the sparse minority samples which are far from the decision boundary. These samples also carry the important information for learning of minority class, if these samples are also considered for oversampling, imbalance ratio will be more reduce also it could enhance the learnability of the classifiers. The outcomes of the proposed approach have been compared with existing oversampling techniques such as SMOTE, Borderline-SMOTE, Safe-level SMOTE, and standard A-SUWO technique in terms of accuracy. As aforementioned, the comparative analysis revealed that the proposed oversampling approach performance increased in average by 5% from 85% to 90% than the existing comparative techniques

    Epilepsy attacks recognition based on 1D octal pattern, wavelet transform and EEG signals

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    Electroencephalogram (EEG) signals have been generally utilized for diagnostic systems. Nowadays artificial intelligence-based systems have been proposed to classify EEG signals to ease diagnosis process. However, machine learning models have generally been used deep learning based classification model to reach high classification accuracies. This work focuses classification epilepsy attacks using EEG signals with a lightweight and simple classification model. Hence, an automated EEG classification model is presented. The used phases of the presented automated EEG classification model are (i) multileveled feature generation using one-dimensional (1D) octal-pattern (OP) and discrete wavelet transform (DWT). Here, main feature generation function is the presented octal-pattern. DWT is employed for level creation. By employing DWT frequency coefficients of the EEG signal is obtained and octal-pattern generates texture features from raw EEG signal and wavelet coefficients. This DWT and octal-pattern based feature generator extracts 128 × 8 = 1024 (Octal-pattern generates 128 features from a signal, 8 signal are used in the feature generation 1 raw EEG and 7 wavelet low-pass filter coefficients). (ii) To select the most useful features, neighborhood component analysis (NCA) is deployed and 128 features are selected. (iii) The selected features are feed to k nearest neighborhood classifier. To test this model, an epilepsy seizure dataset is used and 96.0% accuracy is attained for five categories. The results clearly denoted the success of the presented octal-pattern based epilepsy classification model

    A Review of Physical Human Activity Recognition Chain Using Sensors

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    In the era of Internet of Medical Things (IoMT), healthcare monitoring has gained a vital role nowadays. Moreover, improving lifestyle, encouraging healthy behaviours, and decreasing the chronic diseases are urgently required. However, tracking and monitoring critical cases/conditions of elderly and patients is a great challenge. Healthcare services for those people are crucial in order to achieve high safety consideration. Physical human activity recognition using wearable devices is used to monitor and recognize human activities for elderly and patient. The main aim of this review study is to highlight the human activity recognition chain, which includes, sensing technologies, preprocessing and segmentation, feature extractions methods, and classification techniques. Challenges and future trends are also highlighted.

    Least square-support vector machine based brain tumor classification system with multi model texture features

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    Radiologists confront formidable challenges when confronted with the intricate task of classifying brain tumors through the analysis of MRI images. Our forthcoming manuscript introduces an innovative and highly effective methodology that capitalizes on the capabilities of Least Squares Support Vector Machines (LS-SVM) in tandem with the rich insights drawn from Multi-Scale Morphological Texture Features (MMTF) extracted from T1-weighted MR images. Our methodology underwent meticulous evaluation on a substantial dataset encompassing 139 cases, consisting of 119 cases of aberrant tumors and 20 cases of normal brain images. The outcomes we achieved are nothing short of extraordinary. Our LS-SVM-based approach vastly outperforms competing classifiers, demonstrating its dominance with an exceptional accuracy rate of 98.97%. This represents a substantial 3.97% improvement over alternative methods, accompanied by a notable 2.48% enhancement in Sensitivity and a substantial 10% increase in Specificity. These results conclusively surpass the performance of traditional classifiers such as Support Vector Machines (SVM), Radial Basis Function (RBF), and Artificial Neural Networks (ANN) in terms of classification accuracy. The outstanding performance of our model in the realm of brain tumor diagnosis signifies a substantial leap forward in the field, holding the promise of delivering more precise and dependable tools for radiologists and healthcare professionals in their pivotal role of identifying and classifying brain tumors using MRI imaging techniques

    Utforsking av overgangen fra tradisjonell dataanalyse til metoder med maskin- og dyp læring

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    Data analysis methods based on machine- and deep learning approaches are continuously replacing traditional methods. Models based on deep learning (DL) are applicable to many problems and often have better prediction performance compared to traditional methods. One major difference between the traditional methods and machine learning (ML) approaches is the black box aspect often associated with ML and DL models. The use of ML and DL models offers many opportunities but also challenges. This thesis explores some of these opportunities and challenges of DL modelling with a focus on applications in spectroscopy. DL models are based on artificial neural networks (ANNs) and are known to automatically find complex relations in the data. In Paper I, this property is exploited by designing DL models to learn spectroscopic preprocessing based on classical preprocessing techniques. It is shown that the DL-based preprocessing has some merits with regard to prediction performance, but there is considerable extra effort required when training and tuning these DL models. The flexibility of ANN architecture designs is further studied in Paper II when a DL model for multiblock data analysis is proposed which can also quantify the importance of each data block. A drawback of the DL models is the lack of interpretability. To address this, a different modelling approach is taken in Paper III where the focus is to use DL models in such a way as to retain as much interpretability as possible. The paper presents the concept of non-linear error modelling, where the DL model is used to model the residuals of the linear model instead of the raw input data. The concept is essentially a shrinking of the black box aspect since the majority of the data modelling is done by a linear interpretable model. The final topic explored in this thesis is a more traditional modelling approach inspired by DL techniques. Data sometimes contain intrinsic subgroups which might be more accurately modelled separately than with a global model. Paper IV presents a modelling framework based on locally weighted models and fuzzy partitioning that automatically finds relevant clusters and combines the predictions of each local model. Compared to a DL model, the locally weighted modelling framework is more transparent. It is also shown how the framework can utilise DL techniques to be scaled to problems with huge amounts of data.Metoder basert på maskin- og dyp læring erstatter i stadig økende grad tradisjonell datamodellering. Modeller basert på dyp læring (DL) kan brukes på mange problemer og har ofte bedre prediksjonsevne sammenlignet med tradisjonelle metoder. En stor forskjell mellom tradisjonelle metoder og metoder basert på maskinlæring (ML) er den "svarte boksen" som ofte forbindes med ML- og DL-modeller. Bruken av ML- og DL-modeller åpner opp for mange muligheter, men også utfordringer. Denne avhandlingen utforsker noen av disse mulighetene og utfordringene med DL modeller, fokusert på anvendelser innen spektroskopi. DL-modeller er basert på kunstige nevrale nettverk (KNN) og er kjent for å kunne finne komplekse relasjoner i data. I Artikkel I blir denne egenskapen utnyttet ved å designe DL-modeller som kan lære spektroskopisk preprosessering basert på klassiske preprosesseringsteknikker. Det er vist at DL-basert preprosessering kan være gunstig med tanke på prediksjonsevne, men det kreves større innsats for å trene og justere disse DL-modellene. Fleksibiliteten til design av KNN-arkitekturer er studert videre i Artikkel II hvor en DL-modell for analyse av multiblokkdata er foreslått, som også kan kvantifisere viktigheten til hver datablokk. En ulempe med DL-modeller er manglende muligheter for tolkning. For å adressere dette, er en annen modelleringsframgangsmåte brukt i Artikkel III, hvor fokuset er på å bruke DL-modeller på en måte som bevarer mest mulig tolkbarhet. Artikkelen presenterer konseptet ikke-lineær feilmodellering, hvor en DL-modell blir bruk til å modellere residualer fra en lineær modell i stedet for rå inputdata. Konseptet kan ses på som en krymping av den svarte boksen, siden mesteparten av datamodelleringen er gjort av en lineær, tolkbar modell. Det siste temaet som er utforsket i denne avhandlingen er nærmere en tradisjonell modelleringsvariant, men som er inspirert av DL-teknikker. Data har av og til iboende undergrupper som kan bli mer nøyaktig modellert hver for seg enn med en global modell. Artikkel IV presenterer et modelleringsrammeverk basert på lokalt vektede modeller og "fuzzy" oppdeling, som automatisk finner relevante grupperinger ("clusters") og kombinerer prediksjonene fra hver lokale modell. Sammenlignet med en DL-modell, er det lokalt vektede modelleringsrammeverket mer transparent. Det er også vist hvordan rammeverket kan utnytte teknikker fra DL for å skalere opp til problemer med store mengder data

    Click Fraud Detection in Online and In-app Advertisements: A Learning Based Approach

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    Click Fraud is the fraudulent act of clicking on pay-per-click advertisements to increase a site’s revenue, to drain revenue from the advertiser, or to inflate the popularity of content on social media platforms. In-app advertisements on mobile platforms are among the most common targets for click fraud, which makes companies hesitant to advertise their products. Fraudulent clicks are supposed to be caught by ad providers as part of their service to advertisers, which is commonly done using machine learning methods. However: (1) there is a lack of research in current literature addressing and evaluating the different techniques of click fraud detection and prevention, (2) threat models composed of active learning systems (smart attackers) can mislead the training process of the fraud detection model by polluting the training data, (3) current deep learning models have significant computational overhead, (4) training data is often in an imbalanced state, and balancing it still results in noisy data that can train the classifier incorrectly, and (5) datasets with high dimensionality cause increased computational overhead and decreased classifier correctness -- while existing feature selection techniques address this issue, they have their own performance limitations. By extending the state-of-the-art techniques in the field of machine learning, this dissertation provides the following solutions: (i) To address (1) and (2), we propose a hybrid deep-learning-based model which consists of an artificial neural network, auto-encoder and semi-supervised generative adversarial network. (ii) As a solution for (3), we present Cascaded Forest and Extreme Gradient Boosting with less hyperparameter tuning. (iii) To overcome (4), we propose a row-wise data reduction method, KSMOTE, which filters out noisy data samples both in the raw data and the synthetically generated samples. (iv) For (5), we propose different column-reduction methods such as multi-time-scale Time Series analysis for fraud forecasting, using binary labeled imbalanced datasets and hybrid filter-wrapper feature selection approaches
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