Approach For Classification Of Data Research Articles

PRIVACY BASED CLASSIFICATION MODEL OF PUBLIC DATA BY UTILIZING TWO-STEPS VALIDATION APPROACH

Digital information has become a trend and is integral to modernizing and leveraging various resources in Information Technology (IT). Vast data and information can be obtained anytime and anywhere at our fingertips through ICT facilities. This is considered public data due to its being shared publicly, such as on social media. Public data can be arranged according to various criteria and formats. Users have a right to understand which data can be publicly shared and which data is supposed to be in a private state. However, people always misunderstand and mislead which data needs to be secured and which can be shared. It is further critical when this public data is already exposed to data breaches and data theft. In this work, we propose a data privacy classification approach for public data where this data resides on digital platforms. It aims to inform the public about the level of data privacy before they reveal it on open and free digital platforms. We employed three different privacy classes: low, medium, and high. In response to that, we identified entities of public data that refer to digital information platforms such as websites, mobile apps, and online systems. We then dug further into the data attributes of each entity. The public data attributes are sorted and passed to respondents to obtain their input regarding their decisions on which privacy class is suitable for the respective attribute. Based on the input from respondents, we then used a Naive Bayesian classifier to generate probability weightage for re-assigning the data attributes into the most suitable privacy class. This two-level data classification stage brings better perspectives on data privacy. This modified version of the public data privacy class is then verified by the respondents to analyze their preferences while measuring users’ satisfaction. According to the results, our public data privacy classification model meets public expectations. Optimistically, well-organized data classification contributes to better data practices.

RGAN-EL: A GAN and ensemble learning-based hybrid approach for imbalanced data classification

Imbalanced sample distribution is usually the main reason for the performance degradation of machine learning algorithms. Based on this, this study proposes a hybrid framework (RGAN-EL) combining generative adversarial networks and ensemble learning method to improve the classification performance of imbalanced data. Firstly, we propose a training sample selection strategy based on roulette wheel selection method to make GAN pay more attention to the class overlapping area when fitting the sample distribution. Secondly, we design two kinds of generator training loss, and propose a noise sample filtering method to improve the quality of generated samples. Then, minority class samples are oversampled using the improved RGAN to obtain a balanced training sample set. Finally, combined with the ensemble learning strategy, the final training and prediction are carried out. We conducted experiments on 41 real imbalanced data sets using two evaluation indexes: F1-score and AUC. Specifically, we compare RGAN-EL with six typical ensemble learning; RGAN is compared with three typical GAN models. The experimental results show that RGAN-EL is significantly better than the other six ensemble learning methods, and RGAN is greatly improved compared with three classical GAN models.

Development of an Evaluation Method for Deriving the Water Loss Reduction Factors of Water Distribution Systems: A Case Study in Korean Small and Medium Cities

This study introduces a method that can evaluate the efficiency of leakage management practices and devises a formula to set leakage management goals. To develop the evaluation method for deriving leakage reduction factors, real data from small- and medium-sized cities in South Korea were collected. With the data collected, four leakage management factors (or activities) that could improve revenue water ratio or reduce leakage ratio were identified. With the leakage management factors, correlation analysis was carried out to identify the relationship between independent and dependent variables and within independent variables. Once the relationships were identified, standardization of the data using T-score conversion was carried out to scale all data with different units into similar ranges. Finally, the efficiency of leakage management actions was determined by the formulation of leakage using various data analysis approaches using multiple linear regression analysis and deep neural networks. As a result, pipe replacement was determined as an essential activity to decrease the leakage ratio or increase the revenue water ratio. In addition, annual water loss management actions of the small cities were more actively performed. Furthermore, the performance of data analysis using DNN is more appropriate in data classification, considering the characteristics of time series rather than independent data analysis. Through comparison of the above data classification approaches, the increase or decrease in the leakage ratio/revenue water ratio by the water loss management activity of local water distribution systems can be used to construct a more effective model for classification considering both local and temporal characteristics.

Big Data Classification in IOT Healthcare Application Using Optimal Deep Learning

The Internet of Things (IoT) system is composed of several numbers of sensor nodes and systems, which are wirelessly interlinked to the internet. Generally, big data is the storage of a huge amount of information, which causes the classification process to be very challenging. Numerous big data classification approaches are implemented, but the computational time and secure handling of information are the major problems. The aim of the study is the development of big data approach in Internet of Things (IoT) healthcare application. Hence, this paper presents the proposed Dragonfly Rider Competitive Swarm Optimization-based Deep Residual Network (DRCSO-based DRN) for big data classification in IoT. First, the IoT nodes are simulated, and the heart disease patient data are collected through sensors. The routing is done using the Multi-objective Fractional Gravitational Search Algorithm (Multi-objective FGSA). In the Base Station (BS), the big data classification is done. Here, the classification is done using MapReduce (MR) framework, which includes two phases, like mapper and the reducer phase. The input data is initially fed to the mapper phase in the map-reduce (MR) framework. In the mapper phase, feature selection is carried out based on Dragonfly Rider Optimization Algorithm (DROA) in order to select the appropriate features for further processing. The DROA is modeled through merging Dragonfly Algorithm (DA) and Rider Optimization Algorithm (ROA). In the reducer phase, the classification is performed using DRN, which is trained by the developed DRCSO algorithm. The DRCSO is modeled by incorporating DA, ROA and Competitive Swarm Optimization (CSO). In addition, the performance of the developed method is outperformed than the existing approaches such as Linguistic Fuzzy Rules with Canopy Mapreduce (LFR-CM) + Fuzzy classifier, Machine learning-dependent k-nearest neighbors (FML-KNN), MapReduce-Fuzzy Integral-dependent Ensemble Learning Model+Single hidden layer feedforward neural network (MR-FI-ELM + SLFN) and DROA-recurrent neural network (RNN) based on the accuracy, average residual energy and throughput with the value of 0.929, 0.086[Formula: see text]J and 86.585. The proposed method is used to manage and derive meaningful information from the patient’s medical records, medical examinations results and hospital records.

RBSP-Boosting: A Shapley value-based resampling approach for imbalanced data classification

Addressing the problem of imbalanced data category distribution in real applications and the problem of traditional classifiers tending to ensure the accuracy of the majority class while ignoring the accuracy of the minority class when processing imbalanced data, this paper proposes a method called RBSP-Boosting for imbalanced data classification. First, RBSP-Boosting introduces the Shapley value and calculates the Shapley value for each sample of the dataset through the truncated Monte Carlo method. Moreover, the proposed method removes the noise data according to the Shapley value and undersamples the samples with Shapley values less than zero in the majority class. Then, it takes the Shapley value as the weight of the sample and oversamples the minority class according to the weight. Finally, the new dataset is trained on the classifier through the AdaBoost classifier. Experiments are conducted on nine groups of UCI and KEEL datasets, and RBSP-Boosting is compared with four sampling algorithms: Random-OverSampler, SMOTE, Borderline-SMOTE and SVM-SMOTE. Experimental results show that the RBSP-Boosting method in the three evaluation metrics of AUC, F-score and G-mean, compared with the best performance of the four comparison algorithms, increases by 4.69%, 10.3% and 7.86%, respectively. The proposed method can significantly improve the effect of imbalanced data classification.

Improved multi-class classification approach for imbalanced big data on spark

Improved multi-class classification approach for imbalanced big data on spark

Segment alignment based cross-subject motor imagery classification under fading data

Motor imagery (MI) aims to use brain imagination without actual body activities to support motor learning, and machine learning algorithms such as common spatial patterns (CSP) are proven effective in the analysis of MI signals. In the conventional machine learning-based approaches, there are two main difficulties in feature extraction and recognition of MI signals: high personalization and data fading. The high personalization problem is due to the multi-subject nature when collecting MI signals, and the data fading problem as a recurring issue in MI signal quality is first raised by us but is not widely discussed at present. Aiming to solve the above two mentioned problems, a cross-subject fading data classification approach with segment alignment is proposed to classify the fading data of one single target with the model trained with the normal data of multiple sources in this paper. he effectiveness of proposed method is verified via two experiments: a dataset-based experiment with the dataset from BCI Competition and a lab-based experiment designed and conducted by us. The experimental results obtained from both experiments show that the proposed method can obtain optimal classification performance effectively under different fading levels with data from different subjects.

Sequential clustering and classification using deep learning technique and multi-objective sine-cosine algorithm

This study introduced a novel data analytics-based sequential clustering and classification (SCC) approach. The proposed approach named deep MOSCA-SCC integrates a multi-objective sine–cosine algorithm (MOSCA), deep clustering technique, and classification algorithms to exploit data structure before implementing the prediction model. Herein, the autoencoder combined with the K-means algorithm was utilized for the deep clustering to reveal the data pattern. Regarding classification, support vector machine, back-propagation neural network, and decision tree classification were implemented to explore the correlated factors with the revealed patterns. To evaluate the performance of the proposed method, a comparison was conducted between the proposed deep MOSCA-SCC and other benchmark algorithms, including the NSGAII-SCC and the regular MOSCA-SCC. The deep MOSCA-SCC achieved better performance than other algorithms in terms of clustering sum of squared error and classification accuracy. In addition, deep MOSCA-SCC with a decision tree yielded the best accuracies with 92.6% and 87% when the numbers of clusters were equal to 3 and 4, respectively.

An efficient approach for textual data classification using deep learning.

Text categorization is an effective activity that can be accomplished using a variety of classification algorithms. In machine learning, the classifier is built by learning the features of categories from a set of preset training data. Similarly, deep learning offers enormous benefits for text classification since they execute highly accurately with lower-level engineering and processing. This paper employs machine and deep learning techniques to classify textual data. Textual data contains much useless information that must be pre-processed. We clean the data, impute missing values, and eliminate the repeated columns. Next, we employ machine learning algorithms: logistic regression, random forest, K-nearest neighbors (KNN), and deep learning algorithms: long short-term memory (LSTM), artificial neural network (ANN), and gated recurrent unit (GRU) for classification. Results reveal that LSTM achieves 92% accuracy outperforming all other model and baseline studies.

Retraction Note: A big data classification approach using LDA with an enhanced SVM method for ECG signals in cloud computing

Retraction Note: A big data classification approach using LDA with an enhanced SVM method for ECG signals in cloud computing

Learning a confidence score and the latent space of a new supervised autoencoder for diagnosis and prognosis in clinical metabolomic studies

BackgroundPresently, there is a wide variety of classification methods and deep neural network approaches in bioinformatics. Deep neural networks have proven their effectiveness for classification tasks, and have outperformed classical methods, but they suffer from a lack of interpretability. Therefore, these innovative methods are not appropriate for decision support systems in healthcare. Indeed, to allow clinicians to make informed and well thought out decisions, the algorithm should provide the main pieces of information used to compute the predicted diagnosis and/or prognosis, as well as a confidence score for this prediction.MethodsHerein, we used a new supervised autoencoder (SAE) approach for classification of clinical metabolomic data. This new method has the advantage of providing a confidence score for each prediction thanks to a softmax classifier and a meaningful latent space visualization and to include a new efficient feature selection method, with a structured constraint, which allows for biologically interpretable results.ResultsExperimental results on three metabolomics datasets of clinical samples illustrate the effectiveness of our SAE and its confidence score. The supervised autoencoder provides an accurate localization of the patients in the latent space, and an efficient confidence score. Experiments show that the SAE outperforms classical methods (PLS-DA, Random Forests, SVM, and neural networks (NN)). Furthermore, the metabolites selected by the SAE were found to be biologically relevant.ConclusionIn this paper, we describe a new efficient SAE method to support diagnostic or prognostic evaluation based on metabolomics analyses.

Adaptive Geometric Interval Classifier

Quantile, equal interval, and natural breaks methods are widely used data classification methods in geospatial analysis and cartography. However, when applied to data with skewed distributions, they can only reveal the variations of either high frequent values or extremes, which often leads to undesired and biased classification results. To handle this problem, Esri provided a compromise method, named geometric interval classification (GIC). Although GIC performs well for various classification tasks, its mathematics and solution process remain unclear. Moreover, GIC is theoretically only applicable to single-peak (single-modal), one-dimensional data. This paper first mathematically formulates GIC as a general optimization problem subject to equality constraint. We then further adapt such formulated GIC to handle multi-peak and multi-dimensional data. Both thematic data and remote sensing images are used in this study. The comparison with other classification methods demonstrates the advantage of GIC being able to highlight both middle and extreme values. As such, it can be regarded as a general data classification approach for thematic mapping and other geospatial applications.

A Combined Approach for Multi-Label Text Data Classification.

Automated data analysis solutions are very dependent on data and its quality. The possibility of assigning more than one class to the same data item is one of the specificities that need to be taken into account. There are no solutions, dedicated to Lithuanian text data classification that helps to assign more than one class to data item. In this paper, a new combined approach has been proposed for multilabel text data classification for text analysis. The main aim of the proposed approach is to improve the accuracy of traditional classification algorithms by incorporating the results obtained using similarity measures. The experimental investigation has been performed using the financial news multilabel text data in the Lithuanian language. Data have been collected from four public websites and classified by experts into ten classes manually, where each of the data items has no more than two classes. The results of five commonly used algorithms have been compared for dataset classification: the support vector machine, multinomial naive Bayes, k-nearest neighbours, decision trees, linear and discriminant analysis. In addition, two similarity measures have been compared: the cosine distance and the dice coefficient. Research has shown that the best results have been obtained using the cosine similarity distance and the multinomial naive Bayes classifier. The proposed approach combines the results of these two methods. Research on different cases of the proposed approach indicated the peculiarities of its application. At the same time, the combined approach allowed us to obtain a statistically significant increase in global accuracy.

A classification for complex imbalanced data in disease screening and early diagnosis.

Imbalanced classification has drawn considerable attention in the statistics and machine learning literature. Typically, traditional classification methods often perform poorly when a severely skewed class distribution is observed, not to mention under a high‐dimensional longitudinal data structure. Given the ubiquity of big data in modern health research, it is expected that imbalanced classification in disease diagnosis may encounter an additional level of difficulty that is imposed by such a complex data structure. In this article, we propose a nonparametric classification approach for imbalanced data in longitudinal and high‐dimensional settings. Technically, the functional principal component analysis is first applied for feature extraction under the longitudinal structure. The univariate exponential loss function coupled with group LASSO penalty is then adopted into the classification procedure in high‐dimensional settings. Along with a good improvement in imbalanced classification, our approach provides a meaningful feature selection for interpretation while enjoying a remarkably lower computational complexity. The proposed method is illustrated on the real data application of Alzheimer's disease early detection and its empirical performance in finite sample size is extensively evaluated by simulations.

Development of Decision Tree-Based Framework for Efficient Classification and Prediction of Heart Disease Data

Data mining techniques have been used by several researchers to detect illnesses. Some methods are intended to predict a single sickness, while others are intended to predict a wide variety of diseases. It is also possible to improve the accuracy of sickness prediction. In this post, we provided an overview of data classification approaches that are available. These algorithms are mostly represented by themselves. The classification of data is a common and computationally difficult procedure. A massive amount of information must be analyzed in order to come up with a sound strategy for dealing with disease. There are a number of common scenarios, including as early disease detection, severity assessment, and prediction. This will decrease the progression of the disease, improve the well-being of the patients, and reduce the associated costs of medical care. In this regard, machine learning techniques have been employed. This article presents a machine learning based framework for heart disease data classification and prediction.

New incremental SVM algorithms for human activity recognition in smart homes

Smart homes are equipped with several sensor networks to keep an eye on both residents and their environment, to interpret the current situation and to react immediately. Handling large scale dataset of sensory events on real time to enable efficient interventions is challenging and very difficult. To deal with these data flows and challenges, traditional streaming data classification approaches can be boosted by use of incremental learning. In this paper, we presented two new Incremental SVM methods to improve the performance of SVM classification in the context of human activity recognition tasks. Two feature extraction methods elaborated by refining dependency sensor extraction feature and focusing on the last sensor event only have been suggested. On the other hand, a clustering based approach and a similarity based approach have been suggested to boost learning performance of the incremental SVM algorithms capitalizing on the relationship between data chunk and support vectors of previous chunk. We demonstrate through several simulations on two major publicly available data sets (Aruba and Tulum), the feasibility and improvements in learning and classification performances in real time achieved by our proposed methods over the state-of-the-art. For instance, we have shown that the introduced similarity-based incremental learning is 5 to 9 times faster than other methods in terms of training performances. Similarly, the introduced Last-state sensor feature method induces at least 5% improvement in terms of F1-score when using baseline SVM classifier.

A Salp Swarm-Based Under-Sampling Approach for Medical Imbalanced Data Classification

Data imbalance refers to the unequal distribution of classes within a dataset that directly affects the accuracy of machine learning classification algorithms. Although many resampling techniques have been proposed by researchers, learning from imbalanced data is still considered one of the contemporary challenges. The class imbalanced problem has been complicated as most of the existing techniques don't manage the similarity relationships between minority and majority classes well. In addition, due to the complex relationships among classes, most of the existing techniques do not focus on retaining valuable samples in the majority class(es) properly. In this article, a salp swarm optimization-based under-sampling technique (SSBUT) is proposed to address data class imbalance problems. Utilizing the proposed SSBUT, the similarity relationship among the samples of the majority class is well analyzed, and the samples that do not affect the accuracy of the classification algorithm are eliminated from the majority class. The performance of the proposed SSBUT has been tested on benchmark medical imbalanced datasets and the obtained results have been compared with state-of-the-art under-sampling techniques. The experimental results show that the proposed SSBUT consistently outperformed the state-of-the-art under-sampling techniques in terms of various evaluation criteria.

Combination of Reduction Detection Using TOPSIS for Gene Expression Data Analysis

In high-dimensional data analysis, Feature Selection (FS) is one of the most fundamental issues in machine learning and requires the attention of researchers. These datasets are characterized by huge space due to a high number of features, out of which only a few are significant for analysis. Thus, significant feature extraction is crucial. There are various techniques available for feature selection; among them, the filter techniques are significant in this community, as they can be used with any type of learning algorithm and drastically lower the running time of optimization algorithms and improve the performance of the model. Furthermore, the application of a filter approach depends on the characteristics of the dataset as well as on the machine learning model. Thus, to avoid these issues in this research, a combination of feature reduction (CFR) is considered designing a pipeline of filter approaches for high-dimensional microarray data classification. Considering four filter approaches, sixteen combinations of pipelines are generated. The feature subset is reduced in different levels, and ultimately, the significant feature set is evaluated. The pipelined filter techniques are Correlation-Based Feature Selection (CBFS), Chi-Square Test (CST), Information Gain (InG), and Relief Feature Selection (RFS), and the classification techniques are Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), and k-Nearest Neighbor (k-NN). The performance of CFR depends highly on the datasets as well as on the classifiers. Thereafter, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is used for ranking all reduction combinations and evaluating the superior filter combination among all.

An Optimized Single Layer Perceptron-based Approach for Cardiotocography Data Classification

Uterine Contractions (UC) and Fetal Heart Rate (FHR) are the most common techniques for evaluating fetal and maternal assessment during pregnancy and detecting the changes in fetal oxygenation occurred throughout labor. By monitoring the Cardiotocography (CTG) patterns, doctors can measure fetus state, accelerations, heart rate, and uterine contractions. Several computational and machine learning (ML) methods have been done on CTG recordings to improve the effectiveness of fetus analysis and aid the doctors to understand the variations in their interpretation. However, getting an optimal solution and best accuracy remains an important concern. Among the various ML approaches, artificial neural network (ANN)-based approach has achieved a high performance in several applications. In this paper, an optimized Single Layer Perceptron (SLP)-based approach is proposed to classify the CTG data accurately and predict the fetal state. The approach is able to exploit the advantages of SLP model and optimize the learning rate using a grid search method in which we can arrive at the best accuracy and converge to a local minima. The approach is evaluated on CTG dataset of University of California, Irvine (UCI). The optimized SLP model is trained and tested on the dataset using a 10-fold cross-validation technique to classify the CTG patterns as normal, suspect or pathologic. The experimental results show that the proposed approach achieved 99.20% accuracy compared with the state-of-the-art models.

Functional characterization of prokaryotic dark matter: the road so far and what lies ahead.

Eight-hundred thousand to one trillion prokaryotic species may inhabit our planet. Yet, fewer than two-hundred thousand prokaryotic species have been described. This uncharted fraction of microbial diversity, and its undisclosed coding potential, is known as the "microbial dark matter" (MDM). Next-generation sequencing has allowed to collect a massive amount of genome sequence data, leading to unprecedented advances in the field of genomics. Still, harnessing new functional information from the genomes of uncultured prokaryotes is often limited by standard classification methods. These methods often rely on sequence similarity searches against reference genomes from cultured species. This hinders the discovery of unique genetic elements that are missing from the cultivated realm. It also contributes to the accumulation of prokaryotic gene products of unknown function among public sequence data repositories, highlighting the need for new approaches for sequencing data analysis and classification. Increasing evidence indicates that these proteins of unknown function might be a treasure trove of biotechnological potential. Here, we outline the challenges, opportunities, and the potential hidden within the functional dark matter (FDM) of prokaryotes. We also discuss the pitfalls surrounding molecular and computational approaches currently used to probe these uncharted waters, and discuss future opportunities for research and applications.