Dynamic Classification Problem Research Articles

A Data Stream Dynamic Classification Method with Control of the Decision-making Area

A dynamic classification problem in which a change with time in the data classified is assumed is considered. Data streams, such as computer network data, sensor data, bank transactions, etc., are characterized by problems of data drift, the emergence of new classes, and anomalies. The existing data streams classification methods are analyzed. It is pointed out that there is no a single and effective classification method that would simultaneously take into account the problems of anomaly detection, drift, and model adaptation to new data. The importance of controlling the decision-making area of classifiers for obtaining a high-quality solution of the problem is noted. A dynamic classification method based on a scalable ensemble of autoencoders with a decision-making area controlled using the EDCAP criterion is proposed. The autoencoder properties are used to solve the problems of detecting drift, anomalies and new classes. The ensemble autoencoders were trained to recognize a single class. Based on the EDCAP criterion, the size of the recognition area of each autoencoder was controlled. The classification result is based on analyzing the responses of the ensemble's all autoencoders. When a new class of data is detected, the ensemble is scaled by adding a new autoencoder. When a drift is detected, only the corresponding autoencoders are retrained. The qualities of the proposed dynamic classifier and an incremental algorithm based on an adaptive Hoeffding tree are compared. The advantages of the proposed method are demonstrated on the example of a synthetic data stream that includes drift, a new class, and anomalies.

An improved arithmetic optimization algorithm for training feedforward neural networks under dynamic environments

This paper proposes an improved Arithmetic Optimization Algorithm (AOA) to train artificial neural networks (ANNs) under dynamic environments. Despite many successful applications of metaheuristic training of ANNs, these studies assume static environments, which might not be realistic in real-world nonstationary processes. In this study, the training of ANNs is modeled as a dynamic optimization problem, and the proposed AOA is used to optimize connection weights and biases of the ANN under the presence of concept drift. The proposed method is designed to work for classification tasks. The performance of the proposed algorithm has been tested on twelve dynamic classification problems. Comparative analysis with state-of-the-art metaheuristic optimization algorithms has been provided. The superiority of the compared algorithms has been verified using nonparametric statistical tests. The results show that the improved AOA outperforms compared algorithms in training ANNs under dynamic environments. The findings demonstrate the potential of improved AOA for dynamic data-driven applications.

FMDBN: A first-order Markov dynamic Bayesian network classifier with continuous attributes

With the development of data driven decision making and prediction, time-series data are ubiquitous and the demand for its classification is vast. Although a large body of research has been reported in the literature, it is mainly oriented to situations in which class and attributes are changing simultaneously. In practice however, those class and attributes changes are not always synchronous. This means that further studies for asynchronous classifier problems are necessary. In this paper, a first-order Markov dynamic Bayesian network classifier is proposed to address the asynchronous issue, by combing time-series data preprocessing, time-delayed and dislocated transformation of variables, initial and evolutionary learning. The attribute density in this classifier is estimated based on Gaussian function, and the classification accuracy criterion for time-series progressiveness is also considered. This classifier has a relatively simple structure, which can avoid the problem of overfitting. In addition, data can effectively be classified by utilizing three kinds of classification information, namely time-delayed, non-time-delayed and mixed information in multivariate time-series datasets. The proposed method is also able to accumulate classification information via iterative evolution and thus improve the generalization of classifiers. Experiments were carried out by using standard time-series datasets from UCI, financial and macroeconomic domains. The experimental results show that the proposed first-order Markov dynamic Bayesian network classifier is more accurate in dealing with these dynamic classification problems.

Evaluating deep learning architectures for Speech Emotion Recognition

Speech Emotion Recognition (SER) can be regarded as a static or dynamic classification problem, which makes SER an excellent test bed for investigating and comparing various deep learning architectures. We describe a frame-based formulation to SER that relies on minimal speech processing and end-to-end deep learning to model intra-utterance dynamics. We use the proposed SER system to empirically explore feed-forward and recurrent neural network architectures and their variants. Experiments conducted illuminate the advantages and limitations of these architectures in paralinguistic speech recognition and emotion recognition in particular. As a result of our exploration, we report state-of-the-art results on the IEMOCAP database for speaker-independent SER and present quantitative and qualitative assessments of the models’ performances.

Dynamic classification of ballistic missiles using neural networks and hidden Markov models

This paper addresses dynamic classification of different ranges of ballistic missiles (BM) for air defense application based on kinematic attributes acquired by radars for taking appropriate measures to intercept them. The problem of dynamic classification is formulated using real-time neural network (RTNN) and hidden Markov model (HMM). The idea behind these algorithms is to calculate the output in one pass rather than training and computing over large number of iterations. Besides, to meet the conflicting requirements of classifying small as well as long-range trajectories, we are also proposing a formulation for partitioning the trajectory by using moving window concept. This concept allows us to use parameters in localized frame which helps in handling wide-range of trajectories to fit into the same network. These algorithms are evaluated using the simulated data generated from 6 degree-of-freedom (6DOF) mathematical model, which models missile trajectories. Experimental results show that both the networks are classifying above 95% with real-time neural network outperforming HMM in terms of time of computation on same data. The small classification time enables the use of real-time classification neural network in complex scenario of multi-radar, multi-target engagement by interceptor missiles. To the best of our knowledge this is the first time an attempt is made to classify ballistic missiles using RTNN and HMM.

Flight Segment Identification as a Basis for Pilot Advisory Systems

Flight segment identification is the process of monitoring aircraft state variables and flight events to identify in real time the phase of flight or operational procedure of an aircraft. It is a dynamic classification problem in which the state space is highly dimensional and the boundaries between the various flight phases are not crisply defined. Examples of flight segments include enroute cruise, holding, and initial approach. We explain the role of flight segment identification in building pilot advisory systems, including a new distinction that we propose between state-based flight segment identification and procedural flight segment identification. State-based flight segment identification uses the aircraft state variables to infer the flight operation currently being executed by the pilot. Procedural flight segment identification tracks the flight operation that the pilot should be executing now, based on events and flight rules that are largely outside the control of the pilot. We present one approach to performing flight segment identification based on fuzzy sets and how we applied this solution to the NASA high volume operations concept. Finally, we discuss our results and conclusions from recent flight tests of a flight segment identifier.

FUZZY C-MEANS CLUSTERING MODELS FOR MULTIVARIATE TIME-VARYING DATA: DIFFERENT APPROACHES

The classification of multivariate time-varying data finds application in several fields, such as economics, finance, marketing research, psychometrics, bioinformatics, medicine, signal processing, pattern recognition, etc. In this paper, by considering an exploratory formalization, we propose different unsupervised clustering models for multivariate data time arrays (objects×quantitative variables×times). These models can be classified in two different approaches: the cross sectional and the longitudinal approach. In the first case, after the objects, observed at each time, have been classified, comparison among the classifications made in different time instants will be done. In the second approach, we cluster the time trajectories of the objects; then, we obtain only one classification by comparing the instantaneous and evolutive features of the trajectories of the objects. In particular, in this work, the second approach is analyzed in detail, with reference to the so-called single and double step procedures. Geometric, correlative, instantaneous, evolutive and trend characteristics of the multivariate time arrays are taken into account in the different proposed clustering models. Furthermore, the fuzzy approach, that is particularly suitable in the dynamic classification problem, has been considered. Extensions of a cluster-validity criterion for the proposed fuzzy dynamic clustering models are also suggested. A socio-economic example concludes the paper.

Diagnosing dynamic faults using modular neural nets

The use of radial basis function networks (RBFNs) for diagnosis and classification is discussed. Even though RBFNs can be trained quickly compared to backpropagation networks, the training effort is still significant for large-scale diagnosis problems. Rho-Net, an architecture that decomposes the dynamic classification problem in two ways, making such training tractable, is presented. The first decomposition reduces the amount of training data needed for any stage of the training process by constructing separate networks for each fault class. The second decomposition reduces the dimensionality of the input space by incorporating temporal information at the output of the network, instead of as a temporal window at the input of the net. Application of Rho-Nets to chemical process simulation is discussed. >

A statistical criterion for deciding dynamic classification problems

We give a statistical criterion for deciding whether to reclassify dynamic objects on the basis of empirical distributions of pairwise nearness. To do this we use a generalization of a criterion of Kolmogorov-Smirnov type to the case of partly dependent elements in samples.

A statistical criterion for deciding dynamic classification problems

We give a statistical criterion for deciding whether to reclassify dynamic objects on the basis of empirical distributions of pairwise nearness. To do this we use a generalization of a criterion of Kolmogorov-Smirnov type to the case of partly dependent elements in samples.

Time-constrained reasoning under uncertainty

Dynamic classification problems present unique challenges beyond those of more traditionalstatic knowledge-based systems. Uncertain and incomplete input data, unpredictable event sequences, and critical time and resource constraints require new approaches and techniques for automated reasoning. Our work toward addressing these complex requirements has concentrated on developing an integrated software architecture which supports the knowledge engineering process from development to deployment. The approach we are using to deal with real-time issues in the deployment environment involves the use of a fast knowledge representation scheme, efficient forward and backward chaining mechanisms, and a meta-controller which handles asynchronous inputs, prioritized task requests, and hard performance deadlines.