Approach For Classification Of Data Research Articles

SFMD‐X: A New Functional Data Classifier Based on Shrinkage Functional Mahalanobis Distance

ABSTRACTIn this article, we propose a novel classification approach for functional data based on a shrinkage estimate of functional Mahalanobis distance. We first introduce a new shrinkage functional Mahalanobis distance (SFMD), by using this new distance, we transform the functional observations into a set of vector‐valued pseudo‐samples. Furthermore, we adopt some good classification algorithms designed for multivariate data to this pseudo‐samples instead of the original functional data. The new approach has advantage of highly flexible and scalable, that is, it can easily combine with any classification algorithm, such as support vector machine, tree‐based methods, and neural networks. We demonstrate the performance of the proposed functional classifier through both extensive simulation studies and two real data applications.

Generative dynamical models for classification of rsfMRI data

ABSTRACT The growing availability of large-scale neuroimaging datasets and user-friendly machine learning tools has led to a recent surge in studies that use fMRI data to predict psychological or behavioral variables. Many such studies classify fMRI data on the basis of static features, but fewer try to leverage brain dynamics for classification. Here, we pilot a generative, dynamical approach for classifying resting-state fMRI (rsfMRI) data. By fitting separate hidden Markov models to the classes in our training data and assigning class labels to test data based on their likelihood under those models, we are able to take advantage of dynamical patterns in the data without confronting the statistical limitations of some other dynamical approaches. Moreover, we demonstrate that hidden Markov models are able to successfully perform within-subject classification on the MyConnectome dataset solely on the basis of transition probabilities among their hidden states. On the other hand, individual Human Connectome Project subjects cannot be identified on the basis of hidden state transition probabilities alone—although a vector autoregressive model does achieve high performance. These results demonstrate a dynamical classification approach for rsfMRI data that shows promising performance, particularly for within-subject classification, and has the potential to afford greater interpretability than other approaches.

DNASimCLR: a contrastive learning-based deep learning approach for gene sequence data classification

BackgroundThe rapid advancements in deep neural network models have significantly enhanced the ability to extract features from microbial sequence data, which is critical for addressing biological challenges. However, the scarcity and complexity of labeled microbial data pose substantial difficulties for supervised learning approaches. To address these issues, we propose DNASimCLR, an unsupervised framework designed for efficient gene sequence data feature extraction.ResultsDNASimCLR leverages convolutional neural networks and the SimCLR framework, based on contrastive learning, to extract intricate features from diverse microbial gene sequences. Pre-training was conducted on two classic large scale unlabelled datasets encompassing metagenomes and viral gene sequences. Subsequent classification tasks were performed by fine-tuning the pretrained model using the previously acquired model. Our experiments demonstrate that DNASimCLR is at least comparable to state-of-the-art techniques for gene sequence classification. For convolutional neural network-based approaches, DNASimCLR surpasses the latest existing methods, clearly establishing its superiority over the state-of-the-art CNN-based feature extraction techniques. Furthermore, the model exhibits superior performance across diverse tasks in analyzing biological sequence data, showcasing its robust adaptability.ConclusionsDNASimCLR represents a robust and database-agnostic solution for gene sequence classification. Its versatility allows it to perform well in scenarios involving novel or previously unseen gene sequences, making it a valuable tool for diverse applications in genomics.

Resampling approach for imbalanced data classification based on class instance density per feature value intervals

In practical applications, imbalanced datasets significantly degrade the classification performance of machine learning models. However, most conventional resampling approaches fall short in adequately addressing the varying contributions of individual features to the classification model. In response to this defect, this study introduces three novel resampling approaches. The first approach, Oversampling based on class instance density per feature value intervals (OCF), focuses on augmenting the dataset. The second approach, Undersampling based on class instance density per feature value intervals (UCF), seeks to reduce dataset size. The third approach, Hybrid sampling based on class instance density per feature value intervals (HSCF), which can perform oversampling and undersampling simultaneously. These approaches categorize feature value into different intervals based on their varying information content, calculate class instance densities within these intervals, and generate feature values in intervals with high discriminative information. Subsequently, these generated features are combined to synthesize minority class data, effectively achieving oversampling. Additionally, the study combines class instance density and feature importance to identify majority class data at the classification boundary with minimal contribution and subsequently executes undersampling. The flexibility to adjust sampling ratios and the integration of OCF and UCF enable the implementation of hybrid sampling. Finally, experiments on the benchmark dataset demonstrate the superiority and effectiveness of the proposed method. Furthermore, it is observed that the method proposed in this study enhances the feature dividing capability of decision tree classifiers. Hence, the best results are achieved when working in synergy with decision tree classifiers, leading to the most significant improvements in classification performance. All codes have been published at https://github.com/Wangfeiopen/HSCF.

A novel nearest neighbors algorithm based on power muirhead mean

This paper introduces the innovative Power Muirhead Mean K-Nearest Neighbors (PMM-KNN) algorithm, a novel data classification approach that combines the K-Nearest Neighbors method with the adaptive Power Muirhead Mean operator. The proposed methodology aims to address the limitations of traditional KNN by leveraging the Power Muirhead Mean for calculating the local means of K-nearest neighbors in each class to the query sample. Extensive experimentation on diverse benchmark datasets demonstrates the superiority of PMM-KNN over other classification methods. Results indicate statistically significant improvements in accuracy on various datasets, particularly those with complex and high-dimensional distributions. The adaptability of the Power Muirhead Mean empowers PMM-KNN to effectively capture underlying data structures, leading to enhanced accuracy and robustness. The findings highlight the potential of PMM-KNN as a powerful and versatile tool for data classification tasks, encouraging further research to explore its application in real-world scenarios and the automation of Power Muirhead Mean parameters to unleash its full potential.

How artificial intelligence can enable data classification for market sizing - Insights from applications in practice

Determining the size of the addressable market is a key aspect of market intelligence and requires identifying and delineating projected budget data from potential customers. The market intelligence arena is characterized by a wide range of disparate sources, many of which are unstructured, ranging across competitive, market, financial, and technology sources, and typically necessitating significant manual work to analyze, reconcile, and integrate. The authors present an approach for classification of data from one of these sources, facilitating aggregation and analysis of intelligence information. We describe a concept proof using machine learning that extends a model for automatic mapping of publicly available budget data to segments and subsegments of a market segmentation taxonomy. This approach automates the tagging of market and market segment for each program and cost element by training classification models on the manually labeled historical data. We describe the evaluation and use of multiple natural language processing (NLP) and classification modeling methods. This work's contribution is demonstrating how NLP and machine learning techniques can provide useful data classification and automatic classification even when source data diverges from its specified taxonomic description.

Membership Weight Salp Swarm Algorithm (MWSSA) based feature selection and deep learning approach for breast cancer classification of SNP genomics data

Membership Weight Salp Swarm Algorithm (MWSSA) based feature selection and deep learning approach for breast cancer classification of SNP genomics data

Ice-Jam Investigations along the Oder River Based on Satellite and UAV Data

The Oder River, situated along the border between Poland and Germany, is regularly affected by ice-jam events and their associated hazards, such as a sudden rise in water level and the endangerment to flood-protection infrastructure. The existing databases on past ice-jam events lack substantial information considering ice formation, blockage origins or the spatiotemporal evolution of the ice cover needed for a comprehensive understanding of relevant ice processes. Within this study, the evaluation of satellite and Uncrewed Aerial Vehicle (UAV) data was carried out in order to analyze the capabilities of enhancing river ice information in the study area. Satellite imagery was proven to be a valuable source of investigating ice-jam phenomena on all scales, leading to the identification of initial ice-jam locations, surveying spatiotemporal ice cover evolution or monitoring the maximum ice-cover extent. A simplified approach for river ice classification of satellite radar data using the K-Means Cluster Analysis is introduced, enabling the differentiation between river ice formations. Based on UAV data taken in this study, workflows were presented, allowing for measurements of ice floe velocities and the localization of flooded and ice-covered flow control structures.

A Hybrid Approach for Binary Classification of Imbalanced Data

Binary classification with an imbalanced dataset is challenging. Models tend to consider all samples as belonging to the majority class. Although existing solutions such as sampling methods, cost-sensitive methods, and ensemble learning methods improve the poor accuracy of the minority class, these methods are limited by overfitting or cost parameters that are difficult to decide. This paper proposes a hybrid approach with dimension reduction that consists of data block construction, dimensionality reduction, and ensemble learning with deep neural network classifiers. The performance is evaluated on eight imbalanced public datasets in terms of recall, G-mean, AUC, F-measure, and balanced accuracy. The results show that the proposed model outperforms state-of-the-art methods.

A corpus-based real-time text classification and tagging approach for social data

With the rapid accumulation of large amounts of user-generated content through social media, social data reuse and integration have gained increasing attention recently. This has made it almost obsolete for software applications to collect, store, and work with their own data stored on local servers. While, with the provision of Application Programming Interfaces from the leading social networking sites, data acquisition and integration has become possible, the meaningful usage of such unstructured, non-uniform, and incoherent data collections needs special procedures of data summarization, understanding, and visualization. One particular aspect in this regard that needs special attention is the procedures for data (text snippets in the form of social media posts) categorization and concept tagging to filter out the relevant and most suitable data for the particular audience and for the particular purpose. In this regard, we propose a corpus-based approach for searching and successively categorizing and tagging the social data with relevant concepts in real time. The proposed approach is capable of addressing the semantical and morphological similarities, as well as domain-specific vocabularies of query strings and tagged concepts. We demonstrate the feasibility and application of our proposed approach in a web-based tool that allows searching Facebook posts and provides search results together with a concept map for further navigation, filtering, and refining of search results. The tool has been evaluated by performing multiple search queries, and resultant concept maps and annotated texts are analyzed in terms of their precision. The approach is thereby found effective in achieving its stated goal of classifying text snippets in real time.

Text Semantics-Driven Data Classification Storage Optimization

Data classification storage has emerged as an effective strategy, harnessing the diverse performance attributes of storage devices to orchestrate a harmonious equilibrium between energy consumption, cost considerations, and user accessibility. The traditional strategy of solely relying on access frequency for data classification is no longer suitable for today’s complex storage environment. Diverging from conventional methods, we explore from the perspective of text semantics to address this issue and propose an effective data classification storage method using text semantic similarity to extract seasonal features. First, we adopt a dual-layer strategy based on semantic similarity to extract seasonal features. Second, we put forward a cost-effective data classification storage framework based on text seasonal features. We compare our work with the data classification approach AS-H, which runs at full high performance. In addition, we also compare it with K-ear, which adopts K-means as the classification algorithm. The experimental results show that compared with AS-H and K-ear, our method reduces energy consumption by 9.51–13.35% and operating costs by 13.20–22.17%.

Machine learning approaches for automatic classification of single-particle mass spectrometry data

Abstract. The chemical composition of aerosol particles is a key parameter for human health and climate effects. Single-particle mass spectrometry (SPMS) has evolved to a mature technology with unique chemical coverage and the capability to analyze the distribution of aerosol components in the particle ensemble in real time. With the fully automated characterization of the chemical profile of the aerosol particles, selective real-time monitoring of air quality could be performed, e.g., for urgent risk assessments due to particularly harmful pollutants. For aerosol particle classification, mostly unsupervised clustering algorithms (ART-2a, K-means and their derivatives) are used, which require manual postprocessing. In this work, we focus on supervised algorithms to tackle the problem of the automatic classification of large amounts of aerosol particle data. Supervised learning requires data with labels to train a predictive model. Therefore, we created a labeled benchmark dataset containing ∼ 24 000 particles with eight different coarse categories that were highly abundant at a measurement in summer in Central Europe: elemental carbon (EC), organic carbon and elemental carbon (OC-EC), potassium-rich (K-rich), calcium-rich (Ca-rich), iron-rich (Fe-rich), vanadium-rich (V-rich), magnesium-rich (Mg-rich) and sodium-rich (Na-rich). Using the chemical features of particles, the performance of the following classical supervised algorithms was tested: K-nearest neighbors, support vector machine, decision tree, random forest and multi-layer perceptron. This work shows that despite the entrenched position of unsupervised clustering algorithms in the field, the use of supervised algorithms has the potential to replace the manual step of clustering algorithms in many applications, where real-time data analysis is essential. For the classification of the eight classes, the prediction accuracy of several supervised algorithms exceeded 97 %. The trained model was used to classify ∼ 49 000 particles from a blind dataset in 0.2 s, taking into account also a class of “unclassified” particles. The predictions are highly consistent with the results obtained in a previous study using ART-2a.

Homocentric quadratic surfaces and maximum margin approach for imbalanced data classification

Homocentric quadratic surfaces and maximum margin approach for imbalanced data classification

Golden eagle based improved Att-BiLSTM model for big data classification with hybrid feature extraction and feature selection techniques

ABSTRACT The remarkable development in technology has led to the increase of massive big data. Machine learning processes provide a way for investigators to examine and particularly classify big data. Besides, several machine learning models rely on powerful feature extraction and feature selection techniques for their success. In this paper, a big data classification approach is developed using an optimized deep learning classifier integrated with hybrid feature extraction and feature selection approaches. The proposed technique uses local linear embedding-based kernel principal component analysis and perturbation theory, respectively, to extract more representative data and select the appropriate features from the big data environment. In addition, the feature selection task is fine-tuned by using perturbation theory through heuristic search based on their output accuracy. This feature selection heuristic search method is analysed with five recent heuristic optimization algorithms for deciding the final feature subset. Finally, the data are categorized through an attention-based bidirectional long short-term memory classifier that is optimized with a golden eagle-inspired algorithm. The performance of the proposed model is experimentally verified on publicly accessible datasets. From the experimental outcomes, it is demonstrated that the proposed framework is capable of classifying large datasets with more than 90% accuracy.

Multivariate motion patterns and applications to rainfall radar data

The classification of movement in space is one of the key tasks in environmental science. Various geospatial data such as rainfall or other weather data, data on animal movement or landslide data require a quantitative analysis of the probable movement in space to obtain information on potential risks, ecological developments or changes in future. Usually, machine-learning tools are applied for this task, as these approaches are able to classify large amounts of data. Yet, machine-learning approaches also have some drawbacks, e.g. the often required large training sets and the fact that the algorithms are often hard to interpret. We propose a classification approach for spatial data based on ordinal patterns. Ordinal patterns have the advantage that they are easily applicable, even to small data sets, are robust in the presence of certain changes in the time series and deliver interpretative results. They therefore do not only offer an alternative to machine-learning in the case of small data sets but might also be used in pre-processing for a meaningful feature selection. In this work, we introduce the basic concept of multivariate ordinal patterns and the corresponding limit theorem. A simulation study based on bootstrap demonstrates the validity of the results. The approach is then applied to two real-life data sets, namely rainfall radar data and the movement of a leopard. Both applications emphasize the meaningfulness of the approach. Clearly, certain patterns related to the atmosphere and environment occur significantly often, indicating a strong dependence of the movement on the environment.

Evaluating and comparing bagging and boosting of hybrid learning for breast cancer screening

PurposeIn many fields, including bioinformatics, ensemble methods have been often employed to perform prediction tasks such as classification and regression due to their ability to improve the accuracy and robustness of machine learning models. Given that the most prevalent cancer and the primary cause of death for women is breast cancer, researchers have started using ensemble approaches to advance their studies in this area. Effectively, ensemble methods can be used to develop clinical decision support systems that can help healthcare providers make more accurate judgements. MethodsIn the present study, we construct and evaluate bagging and boosting ensemble methods for the binary classification of breast cancer screening images from the CBIS-DDSM dataset. To build the bagging ensembles, a hybrid architecture consisting of three feature extractors (Inception V3, MobileNet V2, DenseNet 201) and four classifiers (K-nearest neighbors, Multilayer perceptron, Support vector machine, Decision trees) was chosen. As for the boosting ensembles, the method consists of the same three feature extractors with a decision trees-based classifier and the four boosting methods (AdaBoost, GBM, XGboost, LightGBM). ResultsThe evaluation of both ensemble methods over the mammography dataset was made using four performance metrics (accuracy, sensitivity, F1-score, and recall), as well as the Skott–Knott statistical test and the Borda count voting system to rank all the possible model combinations. Results proved that bagging and boosting ensembles generally outperformed both their base learners and single models, and that the best performing ensemble overall was Gradient boosting with DenseNet 201 as feature extractor and 200 trees which scored an accuracy of 85.50 %. ConclusionsThe purpose of this research was to use hybrid learning based ensembles to maximize the potent performance of numerous models. It effectively illustrated the prospective potential of using ML approaches for the binary classification of mammography data used for breast cancer screening.

The Suitability of Machine-Learning Algorithms for the Automatic Acoustic Seafloor Classification of Hard Substrate Habitats in the German Bight

The automatic calculation of sediment maps from hydroacoustic data is of great importance for habitat and sediment mapping as well as monitoring tasks. For this reason, numerous papers have been published that are based on a variety of algorithms and different kinds of input data. However, the current literature lacks comparative studies that investigate the performance of different approaches in depth. Therefore, this study aims to provide recommendations for suitable approaches for the automatic classification of side-scan sonar data that can be applied by agencies and researchers. With random forests, support vector machines, and convolutional neural networks, both traditional machine-learning methods and novel deep learning techniques have been implemented to evaluate their performance regarding the classification of backscatter data from two study sites located in the Sylt Outer Reef in the German Bight. Simple statistical values, textural features, and Weyl coefficients were calculated for different patch sizes as well as levels of quantization and then utilized in the machine-learning algorithms. It is found that large image patches of 32 px size and the combined use of different feature groups lead to the best classification performances. Further, the neural network and support vector machines generated visually more appealing sediment maps than random forests, despite scoring lower overall accuracy. Based on these findings, we recommend classifying side-scan sonar data with image patches of 32 px size and 6-bit quantization either directly in neural networks or with the combined use of multiple feature groups in support vector machines.

A Knowledge Graph-Based Deep Learning Framework for Efficient Content Similarity Search of Sustainable Development Goals Data

ABSTRACT Sustainable development denotes the enhancement of living standards in the present without compromising future generations’ resources. Sustainable Development Goals (SDGs) quantify the accomplishment of sustainable development and pave the way for a world worth living in for future generations. Scholars can contribute to the achievement of the SDGs by guiding the actions of practitioners based on the analysis of SDG data, as intended by this work. We propose a framework of algorithms based on dimensionality reduction methods with the use of Hilbert Space Filling Curves (HSFCs) in order to semantically cluster new uncategorised SDG data and novel indicators, and efficiently place them in the environment of a distributed knowledge graph store. First, a framework of algorithms for insertion of new indicators and projection on the HSFC curve based on their transformer-based similarity assessment, for retrieval of indicators and load-balancing along with an approach for data classification of entrant-indicators is described. Then, a thorough case study in a distributed knowledge graph environment experimentally evaluates our framework. The results are presented and discussed in light of theory along with the actual impact that can have for practitioners analysing SDG data, including intergovernmental organizations, government agencies and social welfare organizations. Our approach empowers SDG knowledge graphs for causal analysis, inference, and manifold interpretations of the societal implications of SDG-related actions, as data are accessed in reduced retrieval times. It facilitates quicker measurement of influence of users and communities on specific goals and serves for faster distributed knowledge matching, as semantic cohesion of data is preserved.

EEG Epileptic Data Classification Using the Schrodinger Operator's Spectrum.

Epilepsy is a common brain disorder characterized by recurrent, unprovoked seizures which affects over 65 million people. Visual inspection of Electroencephalograms (EEG) is common for diagnosis; however, it requires time and expertise. Therefore, an accurate computer-aided epileptic seizure diagnosis system would be valuable. A new research tendency when tackling epileptic seizure detection tends towards minimizing human manual intervention by designing frameworks with autonomous feature engineering. In this optic, this paper proposes a new approach for EEG epileptic data classification. Features derived from the Semi-Classical Signal Analysis (SCSA) method, a quantum-inspired signal processing method well-suited for the characterization of pulse-shaped physiological signals, are proposed. In addition nonlinear dynamical features that proved efficient in characterizing nonlinear dynamics of neural activity have been extracted. Moreover, hyperparameters' optimization, correlation analysis and feature selection have been performed. The selected features are fed into five different machine learning classifiers. The performance of the proposed approach has been analyzed using Bonn university database. The results show that all classifiers yield a performance accuracy of 93% and above.Clinical relevance- The paper contributes to the design of methods and algorithms to build reliable software solutions to assist medical experts and reduce epilepsy disease's diagnosis time and errors.

A novel neural network model with distributed evolutionary approach for big data classification

The considerable improvement of technology produced for various applications has resulted in a growth in data sizes, such as healthcare data, which is renowned for having a large number of variables and data samples. Artificial neural networks (ANN) have demonstrated adaptability and effectiveness in classification, regression, and function approximation tasks. ANN is used extensively in function approximation, prediction, and classification. Irrespective of the task, ANN learns from the data by adjusting the edge weights to minimize the error between the actual and predicted values. Back Propagation is the most frequent learning technique that is used to learn the weights of ANN. However, this approach is prone to the problem of sluggish convergence, which is especially problematic in the case of Big Data. In this paper, we propose a Distributed Genetic Algorithm based ANN Learning Algorithm for addressing challenges associated with ANN learning for Big data. Genetic Algorithm is one of the well-utilized bio-inspired combinatorial optimization methods. Also, it is possible to parallelize it at multiple stages, and this may be done in an extremely effective manner for the distributed learning process. The proposed model is tested with various datasets to evaluate its realizability and efficiency. The results obtained from the experiments show that after a specific volume of data, the proposed learning method outperformed the traditional methods in terms of convergence time and accuracy. The proposed model outperformed the traditional model by almost 80% improvement in computational time.