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Generation of massive data is increasing in big data industries due to the evolution of modern technologies. The big data industries include data source from sensors, Internet of Things, digital and social media. In particular, these big data systems consist of data extraction, preprocessing, integration, analysis, and visualization mechanism. The data encountered from the sources are redundant, incomplete and conflict. Moreover, in real time applications, it is a tedious process for the interpretation of all the data from different sources. In this paper, the gathered data are preprocessed to handle the issues such as redundant, incomplete and conflict. For that, it is proposed to have a generalized dimensionality reduction technique called Shrinkage Linear Discriminate Analysis (SLDA). As a result, the Shrinkage Linear Discriminate Analysis (LDA) will improve the performance of the classifier with generalization. Even though, dimensionality reduction systems improve the performance of the classifier, the irrelevant features get degraded by the performance of the system further. Hence, the relevant and the most important features are selected using Pearson correlation-based feature selection technique which selects the subset of correlated features for improving the performance of the classification system. The selected features are classified using the proposed Quadratic-Gaussian Discriminant Analysis (QGDA) classifier. The proposed evolution techniques are tested with the localization and the cover data sets from machine learning University of California Irvine (UCI) repository. In addition to that, the proposed techniques on datasets are evaluated with the evaluation metrics and compared to the other similar methods which prove the efficiency of the proposed classification system. It has achieved better performance. The acquired accuracy is over 91% for all the experiment on these datasets. Based on the results evaluated in terms of training percentage and mapper, it is meaningful to conclude that the proposed method could be used for big data classification. [ABSTRACT FROM AUTHOR]

Optimizing quality trade-offs in an end-to-end big data science process is challenging, as not only do we need to deal with different types of software components, but also the domain knowledge has to be incorporated along the process. This paper focuses on methods for tackling quality trade-offs in a common data science process for classifying Building Information Modeling (BIM) elements, an important task in the architecture, engineering, and construction industry. Due to the diversity and richness of building elements, machine learning (ML) techniques have been increasingly investigated for classification tasks. However, ML-based classification faces many issues, w.r.t. vast amount of data with heterogeneous data quality, diverse underlying computing configurations, and complex integration with industrial BIM tools, in an end-to-end BIM data analysis. In this paper, we develop an end-to-end ML classification system in which quality of analytics is considered as the first-class feature across different phases, from data collection, feature processing, training to ML model serving. We present our method for studying the quality of analytics trade-offs and carry out experiments with BIM data extracted from Solibri to demonstrate the automation of several tasks in the end-to-end ML classification. Our results have demonstrated that the quality of data, data extraction techniques, and computing configurations must be carefully designed when applying ML classifications for BIM in order to balance constraints of time, cost, and prediction accuracy. Our quality of analytics methods presents generic steps and considerations for dealing with such designs, given the time, cost, and accuracy trade-offs required in specific contexts. Thus, the methods could be applied to the design of end-to-end BIM classification systems using other ML techniques and cloud services. [ABSTRACT FROM AUTHOR]

Inner ear balance problems are common worldwide and are often difficult to diagnose. In this study we examine the classification of patients with inner ear balance problems and controls (people not suffering from inner ear balance problems) based on data derived from the stabilogram signals and using machine learning algorithms. This paper is a continuation for our earlier paper where the same dataset was used and the focus was medically oriented. Our collected dataset consists of stabilogram (a force platform response) data from 30 patients suffering from Ménière's disease and 30 students called controls. We select a wide variety of machine learning algorithms from traditional baseline methods to state-of-the-art methods such as Least-Squares Support Vector Machines and Random Forests. We perform extensive and carefully made parameter value searches and we are able to achieve 88.3% accuracy using k -nearest neighbor classifier. Our results show that machine learning algorithms are well capable of separating patients and controls from each other. [ABSTRACT FROM AUTHOR]

The oddity in large information is rising step by step so that the current programming instruments faces trouble in supervision of huge information. Moreover, the pace of the irregularity information in the immense datasets is a key imperative to the exploration business. Along these lines, this paper proposes a novel method for taking care of the large information utilizing Spark structure. The proposed method experiences two stages for arranging the enormous information, which includes highlight choice and arrangement, which is acted in the underlying hubs of Spark engineering. The proposed improvement calculation is named Rider Chaotic Biography streamlining (RCBO) calculation, which is the incorporation of the Rider Optimization Algorithm (ROA) and the standard confused biogeography-based-advancement (CBBO). The proposed RCBO-profound stacked auto-encoder utilizing Spark structure successfully handles the large information for achieving powerful huge information arrangement. Here, the proposed RCBO is utilized for choosing reasonable highlights from the monstrous dataset. Besides, the profound stacked auto-encoder utilizes RCBO for preparing so as to characterize colossal datasets. In this research we focused on problem of supervision related to big information of The Cover type Data in UCI machine learning repository. The dataset describes the forest cover set data to predict the forest cover type from cartographic variables. The dataset is multivariate in nature with number of web hits 263,361. The number of instances is 581012 with 54 numbers of attributes and the task associated for the dataset is classification. The examination of the proposed RCBO-profound stacked auto-encoder-based Spark structure utilizing the UCI AI datasets uncovered that the proposed technique beat different strategies, by procuring maximal exactness of 86.71%, dice coefficient of 92.7%, affectability of 75.2% and explicitness of 95.4% separately. [ABSTRACT FROM AUTHOR]

INTRODUCTION: The telecommunications industry faces significant challenges due to customer attrition, which directly impacts revenue. To better understand and address this issue, Companies are looking into techniques to determine the internal issues that affect customer churn. OBJECTIVES: This article offers an overview of customer attrition within the telecommunications sector. METHODS: It introduces an advanced churn prediction model harnessing state-of-the-art technologies, including neural networks, machine learning, and other cutting-edge innovations, to achieve remarkably high accuracy rates. By analyzing diverse parameters and datasets collected from multiple telecom companies, valuable insights can be gained. RESULTS: The model's performance on test data can be evaluated using metrics such as Accuracy Score, Area under Curve (AUC), Sensitivity, Specificity, and other performance indicators. CONCLUSION: In order to effectively manage extensive datasets, organizations can leverage Big Data technology. This empowers them to forecast the probability of customer churn and put in place proactive strategies to retain their customer base. [ABSTRACT FROM AUTHOR]

Featured Application: Nowadays, many applications and disciplines work on the basis of stream data. Common examples are the IoT sector (e.g., sensor data analysis), or video, image, and text analysis applications (e.g., in social media analytics or astronomy). With our work, we gather different approaches and terminology, and give a broad overview over the topic. Our main target groups are practitioners and newcomers to the field of data stream classification. Due to the rise of continuous data-generating applications, analyzing data streams has gained increasing attention over the past decades. A core research area in stream data is stream classification, which categorizes or detects data points within an evolving stream of observations. Areas of stream classification are diverse—ranging, e.g., from monitoring sensor data to analyzing a wide range of (social) media applications. Research in stream classification is related to developing methods that adapt to the changing and potentially volatile data stream. It focuses on individual aspects of the stream classification pipeline, e.g., designing suitable algorithm architectures, an efficient train and test procedure, or detecting so-called concept drifts. As a result of the many different research questions and strands, the field is challenging to grasp, especially for beginners. This survey explores, summarizes, and categorizes work within the domain of stream classification and identifies core research threads over the past few years. It is structured based on the stream classification process to facilitate coordination within this complex topic, including common application scenarios and benchmarking data sets. Thus, both newcomers to the field and experts who want to widen their scope can gain (additional) insight into this research area and find starting points and pointers to more in-depth literature on specific issues and research directions in the field. [ABSTRACT FROM AUTHOR]

Abstract: Extreme Learning Machine (ELM) as an emergent technology has shown its promising performance in many applications. This paper proposes a parallelized ELM ensemble based on the Min–Max Modular network (M3-network) to meet the challenge of the so-called big data. The proposed M3-ELM first decomposes classification problems into smaller subproblems, then trains an ELM for each subproblem, and in the end ensembles these ELMs with the M3-network. Twelve data sets including both benchmarks and real-world applications are employed to test the proposed method. The experimental results show that M3-ELM not only speeds up the training phrases by 1.6–4.6 times but also reduces the test errors by 0.37–19.51% compared with the normal ELM. The results also indicate that M3-ELM possesses scalability on large-scale tasks and accuracy improvement on imbalanced tasks. [Copyright &y& Elsevier]

Multi-view ensemble learning has the potential to address issues related to the high dimensionality of data. It attempts to utilize all the relevant only discarding the irrelevant features. The view of a dataset is the sub-table of the training data with respect to a subset of the feature set. The problem of discarding the irrelevant features and obtaining subsets of the relevant features is useful for dimension reduction and dealing with the problem of having fewer training examples than even the reduced set of relevant features. A feature set partitioning resulting in the blocks of relevant features may not yield multiple-view-based classifiers with good classification performance. In this work the optimal feature set partition approach has been proposed. Further, the ensemble learning from views aims to maximize the performance of the classifier. The experiments study the performance of random feature set partitioning, attribute bagging, view generation using attribute clustering, view construction using genetic algorithm and OFSP proposed method. The blocks of relevant feature subsets are used to construct the multi-view classifier ensemble using K-nearest neighbor, Naïve Bayesian and support vector machine algorithm applied to sixteen high-dimensional data sets from UCI machine learning repository. The performance parameters considered for comparison are classification accuracy, disagreement among the classifiers, execution time and percentage reduction of attributes. [ABSTRACT FROM AUTHOR]