Data Stream Classification Research Articles

Invisible experience to real-time assessment in elite tennis athlete training: Sport-specific movement classification based on wearable MEMS sensor data

This study examined the reliability of a tennis stroke classification and assessment platform consisting of a single low-cost MEMS sensor in a wrist-worn wearable device, smartphone, and computer. The data that was collected was transmitted via Bluetooth and analyzed by machine learning algorithms. Twelve right-handed male elite tennis athletes participated in the study, and each athlete performed 150 strokes. The results from three machine learning algorithms regarding their recognition and classification of the real-time data stream were compared. Stroke recognition and classification went through pre-processing, segmentation, feature extraction, and classification with Support Vector Machine (SVM), including SVM without normalization, SVM with Min–Max, SVM with Z-score normalization, K-nearest neighbor (K-NN), and Naive Bayes (NB) machine learning algorithms. During the data training process, 10-fold cross-validation was used to avoid overfitting and suitable parameters were found within the SVM classifiers. The best classifier was achieved when C = 1 using the RBF kernel function. Different machine learning algorithms’ classification of unique stroke types yielded highly reliable clusters within each stroke type with the highest test accuracy of 99% achieved by SVM with Min–Max normalization and 98.4% achieved using SVM with a Z-score normalization classifier.

Enhanced Data Stream Classification by Optimized Weight Updated Meta-learning: Continuous learning-based on Concept-Drift

Purpose The aim of continuous learning is to obtain and fine-tune information gradually without removing the already existing information. Many conventional approaches in streaming data classification assume that all arrived new data is completely labeled. To regularize Neural Networks (NNs) by merging side information like user-provided labels or pair-wise constraints, incremental semi-supervised learning models need to be introduced. However, they are hard to implement, specifically in non-stationary environments because of the efficiency and sensitivity of such algorithms to parameters. The periodic update and maintenance of the decision method is the significant challenge in incremental algorithms whenever the new data arrives. Design/methodology/approach Hence, this paper plans to develop the meta-learning model for handling continuous or streaming data. Initially, the data pertain to continuous behavior is gathered from diverse benchmark source. Further, the classification of the data is performed by the Recurrent Neural Network (RNN), in which testing weight is adjusted or optimized by the new meta-heuristic algorithm. Here, the weight is updated for reducing the error difference between the target and the measured data when new data is given for testing. The optimized weight updated testing is performed by evaluating the concept-drift and classification accuracy. The new continuous learning by RNN is accomplished by the improved Opposition-based Novel Updating Spotted Hyena Optimization (ONU-SHO). Finally, the experiments with different datasets show that the proposed learning is improved over the conventional models. Findings From the analysis, the accuracy of the ONU-SHO based RNN (ONU-SHO-RNN) was 10.1% advanced than Decision Tree (DT), 7.6% advanced than Naive Bayes (NB), 7.4% advanced than k-nearest neighbors (KNN), 2.5% advanced than Support Vector Machine (SVM) 9.3% advanced than NN, and 10.6% advanced than RNN. Hence, it is confirmed that the ONU-SHO algorithm is performing well for acquiring the best data stream classification. Originality/value This paper introduces a novel meta-learning model using Opposition-based Novel Updating Spotted Hyena Optimization (ONU-SHO)-based Recurrent Neural Network (RNN) for handling continuous or streaming data. This is the first work utilizes a novel meta-learning model using Opposition-based Novel Updating Spotted Hyena Optimization (ONU-SHO)-based Recurrent Neural Network (RNN) for handling continuous or streaming data.

Dynamically Adjusting Diversity in Ensembles for the Classification of Data Streams with Concept Drift

A data stream can be defined as a system that continually generates a lot of data over time. Today, processing data streams requires new demands and challenging tasks in the data mining and machine learning areas. Concept Drift is a problem commonly characterized as changes in the distribution of the data within a data stream. The implementation of new methods for dealing with data streams where concept drifts occur requires algorithms that can adapt to several scenarios to improve its performance in the different experimental situations where they are tested. This research proposes a strategy for dynamic parameter adjustment in the presence of concept drifts. Parameter Estimation Procedure (PEP) is a general method proposed for dynamically adjusting parameters which is applied to the diversity parameter (λ) of several classification ensembles commonly used in the area. To this end, the proposed estimation method (PEP) was used to create Boosting-like Online Learning Ensemble with Parameter Estimation (BOLE-PE), Online AdaBoost-based M1 with Parameter Estimation (OABM1-PE), and Oza and Russell’s Online Bagging with Parameter Estimation (OzaBag-PE), based on the existing ensembles BOLE, OABM1, and OzaBag, respectively. To validate them, experiments were performed with artificial and real-world datasets using Hoeffding Tree (HT) as base classifier. The accuracy results were statistically evaluated using a variation of the Friedman test and the Nemenyi post-hoc test. The experimental results showed that the application of the dynamic estimation in the diversity parameter (λ) produced good results in most scenarios, i.e., the modified methods have improved accuracy in the experiments with both artificial and real-world datasets.

Incremental semi-supervised Extreme Learning Machine for Mixed data stream classification

With an explosive growth of data generated in the Internet and other fields, the data stream classification has sparked broad interest recently. Nowadays, some of the challenges in data streams, such as concept drift detection and supervised data stream classification, have been well-developed. However, when confronted with mixed data streams (containing categorical and numerical values) or limited available labeled samples, many data stream methods cannot achieve a satisfying performance or even cannot work. To tackle these two problems, we proposed an Incremental Semi-supervised Extreme Learning Machine for Mixed data stream classification (MIS-ELM). To be specific, for the issue of mixed data in data streams, we designed a novel soft one-hot encoding method by combining the coupling object similarity method and the one-hot encoding method, which can embed categorical data into high-quality numerical data and is used in the data preprocessing phase of MIS-ELM; for the issue of limited labeled samples, we introduced an incremental learning method based on unlabeled data, which is employed in the training classifier phase of MIS-ELM. When no concept drift occurs in the data stream, MIS-ELM uses only unlabeled data for incremental learning to fine-tune the classifier trained in the previous sliding window. Also, MIS-ELM instinctively inherits the fast computability of ELM, so it is very suitable for the real-time processing of data streams. Finally, we evaluated the representation performance of the soft one-hot encoding and the classification performance of MIS-ELM, within real data streams. The experimental results demonstrate the superiority of the proposed methods over the state-of-the-art techniques in their areas, respectively.

Selective ensemble-based online adaptive deep neural networks for streaming data with concept drift

Concept drift is an important issue in the field of streaming data mining. However, how to maintain real-time model convergence in a dynamic environment is an important and difficult problem. In addition, the current methods have limited ability to deal with the problem of streaming data classification for complex nonlinear problems. To solve these problems, a selective ensemble-based online adaptive deep neural network (SEOA) is proposed to address concept drift. First, the adaptive depth unit is constructed by combining shallow features with deep features and adaptively controls the information flow in the neural network according to changes in streaming data at adjacent moments, which improves the convergence of the online deep learning model. Then, the adaptive depth units of different layers are regarded as base classifiers for ensemble and weighted dynamically according to the loss of each classifier. In addition, a dynamic selection of base classifiers is adopted according to the fluctuation of the streaming data to achieve a balance between stability and adaptability. The experimental results show that the SEOA can effectively contend with different types of concept drift and has good robustness and generalization.

Trampoline Motion Decomposition Method Based on Deep Learning Image Recognition

The automatic segmentation and classification of an unknown motion data stream based on given motion classes constitute an important research problem with applications in computer vision, animation, healthcare, and sports sciences. In this paper, the scenario of trampoline motions is considered, where an athlete performs a routine consisting of sequence of jumps that belong to predefined motion classes such as somersaults. The purpose of this study was to make theoretical discussions on the turning starting time and starting technique of trampoline somersault based on image recognition and point out that the appropriate turning starting time of trampoline somersault is the event when the spring net of the trampoline recovers and applies force to the human body, and the overturning start exists in the latter half of the take-off action. It is considered that how to obtain the ideal full reaction force of the net facing the human body is the flip starting technique. This work analyzes the key steps and events for trampoline somersaults and the application of artificial intelligence for the recognition of actions in the healthcare and sports fields. The effectiveness of the proposed study is shown through experimental results. The study can facilitate the process of recognition of trampoline somersault.

A Multi-Swarm ABC Algorithm for Parameters Optimization of SOFM Neural Network in Dynamic Environment

Self-organizing feature map (SOFM) neural network is a kind of competitive unsupervised learning neural network, which has strong self-organizing and self-learning capabilities. It has been widely used in the fields of data classification and data clustering. A crucial step for SOFM neural network is to set its weight parameters correctly because the output accuracy and efficiency of the network depend much on these parameters. Most of current methods for parameter setting are based on static data. However, in a dynamic environment, the statistical characteristics of the generated data will change unpredictably over time. If the SOFM network cannot react to the changes of the environment, its performance will degrade. To deal with this problem, a more powerful multi-swarm artificial bee colony algorithm (MABC) is proposed. In the algorithm, the classic ABC algorithm is improved with multi-swarm and exclusive operation strategies to make it suitable for tracking optimal parameter settings of the SOFM network, so that the SOFM network can be applied to a dynamic environment. Two real data streams, which are regarded as coming from dynamic environments, are used to evaluate the effectiveness of the algorithm. Results show that the proposed algorithm is superior to the classic SOFM algorithm in terms of clustering purity and effectiveness. It is a promising method for the classification of data streams from dynamic environments.

Analyzing and repairing concept drift adaptation in data stream classification

Data collected over time often exhibit changes in distribution, or concept drift, caused by changes in factors relevant to the classification task, e.g. weather conditions. Incorporating all relevant factors into the model may be able to capture these changes, however, this is usually not practical. Data stream based methods, which instead explicitly detect concept drift, have been shown to retain performance under unknown changing conditions. These methods adapt to concept drift by training a model to classify each distinct data distribution. However, we hypothesize that existing methods do not robustly handle real-world tasks, leading to adaptation errors where context is misidentified. Adaptation errors may cause a system to use a model which does not fit the current data, reducing performance. We propose a novel repair algorithm to identify and correct errors in concept drift adaptation. Evaluation on synthetic data shows that our proposed AiRStream system has higher performance than baseline methods, while is also better at capturing the dynamics of the stream. Evaluation on an air quality inference task shows AiRStream provides increased real-world performance compared to eight baseline methods. A case study shows that AiRStream is able to build a robust model of environmental conditions over this task, allowing the adaptions made to concept drift to be analysed and related to changes in weather. We discovered a strong predictive link between the adaptions made by AiRStream and changes in meteorological conditions.

A deep fast learning framework towards exploring Imbalanced data and Multi-class Drift in Evolving Data Streams

Data stream classification poses great challenges in the text based data mining community towards handling evolving data stream. Identification of feature evolution and imbalanced data on the class generated is an important research area for data stream classification on employing of traditional machine learning classifiers. Class evolution and drift is the phenomenon of class emergence and disappearance. Due to class evolutions, performance of the learning model degrades drastically over time. Class evolution problem has been handled on analysis of feature drift and multi class drift. Multi-class drift occur according to probability and time, is categorized as sudden, gradual and recurring drift. Multi class drift has been captured by proposing a new framework in this paper which is named as “Deep Fast Learning Framework”. Initially feature has been extracted using ensemble of technique such as Incremental Kernel Principle Component Analysis, Incremental linear Discriminant analysis and Incremental Linear Principle Component Analysis. These techniques for feature extraction have treated as online feature extraction process. Extracted feature has been processed in the deep fast learning classifier framework which is composed of hybrid ensemble classifiers which follows chuck based ensemble and online ensemble classifiers in parallel on basis of gradual class evolution on block of data on the data streams in form of features. Base learner or classifier has been established using deep neural network to generate the fast learning model on deep analysis of the features obtained and its relationship with existing classes on continuously updating the learner by replacing the older model with newly trained model. Further Base learner will remove the emerging classes which is least utilized and detect the recurring classes on basis of the feature obtained easily. This model is effective in determining the novel classes and recurring class to features which has the possibility of multi class drift. Finally class imbalance problem has been handled on employing under sampling method for base learning model. Experimental results has proved the superiority of the proposed framework on benchmark dataset against state of art approaches on the performance measures such as precision , recall and f measure.

Classification of Imbalanced Data Stream: Techniques and Challenges

As the number of generated data increases every day, this has brought the importance of data mining and knowledge extraction. In traditional data mining, offline status can be used for knowledge extraction. Nevertheless, dealing with stream data mining is different due to continuously arriving data that can be processed at a single scan besides the appearance of concept drift. As the pre-processing stage is critical in knowledge extraction, imbalanced stream data gain significant popularity in the last few years among researchers. Many real-world applications suffer from class imbalance including medical, business, fraud detection and etc. Learning from the supervised model includes classes whether it is binary- or multi-classes. These classes are often imbalance where it is divided into the majority (negative) class and minority (positive) class, which can cause a bias toward the majority class that leads to skew in predictive performance models. Handles imbalance streaming data is mandatory for more accurate and reliable learning models. In this paper, we will present an overview of data stream mining and its tools. Besides, summarize the problem of class imbalance and its different approaches. In addition, researchers will present the popular evaluation metrics and challenges prone from imbalanced streaming data.

Recurring Drift Detection and Model Selection-Based Ensemble Classification for Data Streams with Unlabeled Data

Data stream classification is widely popular in the field of network monitoring, sensor network and electronic commerce, etc. However, in the real-world applications, recurring concept drifting and label missing in data streams seriously aggravate the difficulty on the classification solutions. And this challenge has received little attention from the research community. Motivated by this, we propose a new ensemble classification approach based on the recurring concept drifting detection and model selection for data streams with unlabeled data. First, we build an ensemble model based on the classifiers and clusters. To improve the classification accuracy, we use the ensemble model to predict each data chunk and partition clusters according to the distribution of predicted class labels. Second, we adopt a new concept drifting detection method based on the divergence of concept distributions between adjoining data chunks to distinguish recurring concept drifts. All historical new concepts will be maintained. Meanwhile, we introduce the time-stamp-based weights for base models in the ensemble model. In the selection of the base model, we consider the time-stamp-based weight and the divergence between concept distributions simultaneously. Finally, extensive experiments conducted on four benchmark data sets show that our approach can quickly adapt to data streams with recurring concept drifts, and improve the classification accuracy compared to several state-of-the-art classification algorithms for data streams with concept drifts and unlabeled data.

Efficient Model for Privacy Preserving Classification Of Data Streams

Privacy preserving data mining has become progressively mainstream since it permits sharing of privacy delicate data for examination purposes .So individuals have gotten progressively reluctant to share their data, regularly bringing about people either declining to share their data or giving inaccurate data. As of late, privacy preserving data mining has been concentrated broadly, on account of the wide multiplication of delicate data on the web. Data Mining manages programmed extraction of already obscure examples from a lot of data sets. These data sets ordinarily contain touchy individual data or basic business data, which thusly get presented to different gatherings during Data Mining exercises. This makes hindrance in Data Mining measure. Answer for this issue is given by Privacy preserving in data mining (PPDM). PPDM is a specific arrangement of Data Mining exercises where procedures are developed to secure privacy of the data, so the information revelation cycle can be completed without obstruction. The target of PPDM is to shield delicate data from spilling in the mining cycle alongside exact Data Mining results. The objective of this paper is to introduce the survey on different privacy preserving strategies which are useful in mining huge measure of data with sensible productivity and security.

Classification of the drifting data streams using heterogeneous diversified dynamic class-weighted ensemble.

Data streams can be defined as the continuous stream of data coming from different sources and in different forms. Streams are often very dynamic, and its underlying structure usually changes over time, which may result to a phenomenon called concept drift. When solving predictive problems using the streaming data, traditional machine learning models trained on historical data may become invalid when such changes occur. Adaptive models equipped with mechanisms to reflect the changes in the data proved to be suitable to handle drifting streams. Adaptive ensemble models represent a popular group of these methods used in classification of drifting data streams. In this paper, we present the heterogeneous adaptive ensemble model for the data streams classification, which utilizes the dynamic class weighting scheme and a mechanism to maintain the diversity of the ensemble members. Our main objective was to design a model consisting of a heterogeneous group of base learners (Naive Bayes, k-NN, Decision trees), with adaptive mechanism which besides the performance of the members also takes into an account the diversity of the ensemble. The model was experimentally evaluated on both real-world and synthetic datasets. We compared the presented model with other existing adaptive ensemble methods, both from the perspective of predictive performance and computational resource requirements.

Research on Mining of Applied Mathematics Educational Resources Based on Edge Computing and Data Stream Classification

Facing the massive data of higher education institutions, data mining technology is an intelligent information processing technology that can effectively discover knowledge from the massive data and can discover important information that people have previously ignored from the huge data information. This article is dedicated to the development of applied mathematics education resource mining technology based on edge computing and data stream classification. First of all, this article establishes a resource system architecture suitable for existing applied mathematics education through edge computing technology, which can effectively improve the efficiency of data mining. Secondly, the data stream classification algorithm is used for information extraction and classification integration of massive applied mathematical education data. This method provides potential and valuable information for decision-makers and education practitioners. Finally, the simulation and performance test of the system verify that it has the functions of mathematical information mining and data processing. This system will provide strong support for applied mathematics education reform.

Research on English Movie Resource Information Mining Based on Dynamic Data Stream Classification

Massive English film and television resources will provide people with a rich content, while it has become more and more difficult for people to find the resources and information they need. Through the analysis of massive amounts of film and television data, the application system can effectively push the works that users may like. In response to this problem, we can build an English film and television resource information mining model by combining fuzzy neural network algorithms and dynamic data stream classification technology. Firstly, we use dynamic data stream classification technology to preprocess and screen English film and television resource information. Secondly, we use the fuzzy neural network algorithm to conduct data mining on related film and television resource information. The experimental results verify the superior performance of the English film and television resource information mining model established in this paper. This model can help people to find the resources and information they need.

A theoretical framework for classifying occupant-centric data streams on indoor climate using a physiological and cognitive process hierarchy

New and pervasive information and communication technologies have made it possible to capture a large range of continuous data from, or close to, each individual building occupant. These occupant-centric data streams may include subjective votes, evaluations, complaints, control actions, physiological measurements such as heart rate or pupil size, physical measurements of skin temperature or local draft and air temperature measurements, and much more. Currently, considerable resources are put into studies that focus on the development and potential uses of such systems, while the origin and nature of the collected information which is embedded in the data is poorly investigated. In this paper, we propose a taxonomy for the classification of occupant-centric data streams, developed through the application of established theories and categories in environmental and market psychology. The proposed framework organises five data source categories and links them to four levels of physiological and cognitive processes, making an explicit connection between data and embedded information attributes. The framework, originally developed to classify continuous occupant centric data in the domain of indoor climate, can also bring insights that might help explain known gaps and challenges in different models and theories that aim at predicting individual satisfaction with indoor climate conditions.

Chi-MFlexDT:Chi-square-based multi flexible fuzzy decision tree for data stream classification

In recent years, a number of classification algorithms using fuzzy decision trees have been proposed. A key issue encountered in all of these algorithms is how to select the division feature. In traditional algorithms, it is common to use the information gain criterion. There is a claim that the use of information-based criteria leads to an unfair selection of features that have large number of values while not being suitable for division. This problem causes to decrease accuracy of classification in stream data. However, other algorithms for classifying stream data and extracting fuzzy rules have been proposed but the problem of unfairly selecting features with large amount of values still persists. This paper, present a new algorithm to solve problems of selecting splitting feature in fuzzy decision tree for classification of stream data. In proposed algorithm, we extend multi flexible fuzzy decision tree (MFlexDT) with chi-square based fuzzy partitioning of values. In this paper, we also extend traditional chi-square statistical to fuzzy chi-square statistical data distribution. In evaluating the proposed algorithm, tree depth and accuracy are two factors that affect performance. Experimental results show that the new algorithm has been able to improve the performance of other algorithms.

A novel semi-supervised ensemble algorithm using a performance-based selection metric to non-stationary data streams

In this article, we consider the semi-supervised data stream classification problems. Most of the semi-supervised learning algorithms suffer from a proper selection metric to select from the newly-labeled data points through the training procedure. These approaches mainly employ the probability estimation of the underlying base learners to their predictions as a selection metric, which is not optimal in many cases. Handling different kinds of concept drifts is another issue in data streams. Considering these issues, we propose a novel Semi-Supervised Ensemble algorithm using a Performance-Based Selection metric to data streams, named SSE-PBS. The proposed selection metric is based on a pseudo-accuracy and energy regularization factor. We show that SSE-PBS improves classification performance and handles different kinds of concept drifts. The proposed algorithm can also employ any kind of incremental base learners. In the experiments, we report the results of two base learners on synthetic and real-world datasets. The experiments show that SSE-PBS significantly improves the classification performance of the used underlying base learners. Furthermore, we compare the results to the state-of-the-art supervised and semi-supervised approaches in data streams. The results further show that SSE-PBS outperforms the other methods when there is a small portion of labeled instances.

Self-organizing fuzzy inference ensemble system for big streaming data classification

An evolving intelligent system (EIS) is able to self-update its system structure and meta-parameters from streaming data. However, since the majority of EISs are implemented on a single-model architecture, their performances on large-scale, complex data streams are often limited. To address this deficiency, a novel self-organizing fuzzy inference ensemble framework is proposed in this paper. As the base learner of the proposed ensemble system, the self-organizing fuzzy inference system is capable of self-learning a highly transparent predictive model from streaming data on a chunk-by-chunk basis through a human-interpretable process. Very importantly, the base learner can continuously self-adjust its decision boundaries based on the inter-class and intra-class distances between prototypes identified from successive data chunks for higher classification precision. Thanks to its parallel distributed computing architecture, the proposed ensemble framework can achieve great classification precision while maintain high computational efficiency on large-scale problems. Numerical examples based on popular benchmark big data problems demonstrate the superior performance of the proposed approach over the state-of-the-art alternatives in terms of both classification accuracy and computational efficiency.

Hellinger Distance Weighted Ensemble for imbalanced data stream classification

The imbalanced data classification remains a vital problem. The key is to find such methods that classify both the minority and majority class correctly. The paper presents the classifier ensemble for classifying binary, non-stationary and imbalanced data streams where the Hellinger Distance is used to prune the ensemble. The paper includes an experimental evaluation of the method based on the conducted experiments. The first one checks the impact of the base classifier type on the quality of the classification. In the second experiment, the Hellinger Distance Weighted Ensemble (HDWE) method is compared to selected state-of-the-art methods using a statistical test with two base classifiers. The method was profoundly tested based on many imbalanced data streams and obtained results proved the HDWE method's usefulness.