Parsimonious Random Vector Functional Link Network for Data Streams

The majority of the existing work on random vector functional link networks (RVFLNs) is not scalable for data stream analytics because they work under a batch learning scenario and lack a self-organizing property. A novel RVLFN, namely the parsimonious random vector functional link network (pRVFLN), is proposed in this paper. pRVFLN adopts a fully flexible and adaptive working principle where its network structure can be configured from scratch and can be automatically generated, pruned and recalled from data streams. pRVFLN is capable of selecting and deselecting input attributes on the fly as well as capable of extracting important training samples for model updates. In addition, pRVFLN introduces a non-parametric type of hidden node which completely reflects the real data distribution and is not constrained by a specific shape of the cluster. All learning procedures of pRVFLN follow a strictly single-pass learning mode, which is applicable for online time-critical applications. The advantage of pRVFLN is verified through numerous simulations with real-world data streams. It was benchmarked against recently published algorithms where it demonstrated comparable and even higher predictive accuracies while imposing the lowest complexities

An Incremental Construction of Deep Neuro Fuzzy System for Continual Learning of Non-stationary Data Streams

Existing FNNs are mostly developed under a shallow network configuration having lower generalization power than those of deep structures. This paper proposes a novel self-organizing deep FNN, namely DEVFNN. Fuzzy rules can be automatically extracted from data streams or removed if they play limited role during their lifespan. The structure of the network can be deepened on demand by stacking additional layers using a drift detection method which not only detects the covariate drift, variations of input space, but also accurately identifies the real drift, dynamic changes of both feature space and target space. DEVFNN is developed under the stacked generalization principle via the feature augmentation concept where a recently developed algorithm, namely gClass, drives the hidden layer. It is equipped by an automatic feature selection method which controls activation and deactivation of input attributes to induce varying subsets of input features. A deep network simplification procedure is put forward using the concept of hidden layer merging to prevent uncontrollable growth of dimensionality of input space due to the nature of feature augmentation approach in building a deep network structure. DEVFNN works in the sample-wise fashion and is compatible for data stream applications. The efficacy of DEVFNN has been thoroughly evaluated using seven datasets with non-stationary properties under the prequential test-then-train protocol. It has been compared with four popular continual learning algorithms and its shallow counterpart where DEVFNN demonstrates improvement of classification accuracy. Moreover, it is also shown that the concept drift detection method is an effective tool to control the depth of network structure while the hidden layer merging scenario is capable of simplifying the network complexity of a deep network with negligible compromise of generalization performance.Comment: This paper has been published in IEEE Transactions on Fuzzy System

Deep Stacked Stochastic Configuration Networks for Lifelong Learning of Non-Stationary Data Streams

The concept of SCN offers a fast framework with universal approximation guarantee for lifelong learning of non-stationary data streams. Its adaptive scope selection property enables for proper random generation of hidden unit parameters advancing conventional randomized approaches constrained with a fixed scope of random parameters. This paper proposes deep stacked stochastic configuration network (DSSCN) for continual learning of non-stationary data streams which contributes two major aspects: 1) DSSCN features a self-constructing methodology of deep stacked network structure where hidden unit and hidden layer are extracted automatically from continuously generated data streams; 2) the concept of SCN is developed to randomly assign inverse covariance matrix of multivariate Gaussian function in the hidden node addition step bypassing its computationally prohibitive tuning phase. Numerical evaluation and comparison with prominent data stream algorithms under two procedures: periodic hold-out and prequential test-then-train processes demonstrate the advantage of proposed methodology.Comment: This paper has been published in Information Science

A randomized neural network for data streams

© 2017 IEEE. Randomized neural network (RNN) is a highly feasible solution in the era of big data because it offers a simple and fast working principle in processing dynamic and evolving data streams. This paper proposes a novel RNN, namely recurrent type-2 random vector functional link network (RT2McRVFLN), which provides a highly scalable solution for data streams in a strictly online and integrated framework. It is built upon the psychologically inspired concept of metacognitive learning, which covers three basic components of human learning: what-to-learn, how-to-learn, and when-to-learn. The what-to-learn selects important samples on the fly with the use of online active learning scenario, which renders our algorithm an online semi-supervised algorithm. The how-to-learn process combines an open structure of evolving concept and a randomized learning algorithm of random vector functional link network (RVFLN). The efficacy of the RT2McRVFLN has been numerically validated through two real-world case studies and comparisons with its counterparts, which arrive at a conclusive finding that our algorithm delivers a tradeoff between accuracy and simplicity

A Novel Meta-Cognitive Extreme Learning Machine to Learning from Data Streams

© 2015 IEEE. Extreme Learning Machine (ELM) is an answer to an increasing demand for a low-cost learning algorithm to handle big data applications. Nevertheless, existing ELMs leave four uncharted problems: complexity, uncertainty, concept drifts, curse of dimensionality. To correct these issues, a novel incremental meta-cognitive ELM, namely Evolving Type-2 Extreme Learning Machine (eT2ELM), is proposed. Et2Elm is built upon the three pillars of meta-cognitive learning, namely what-To-learn, how-To-learn, when-To-learn, where the notion of ELM is implemented in the how-To-learn component. On the other hand, eT2ELM is driven by a generalized interval type-2 Fuzzy Neural Network (FNN) as the cognitive constituent, where the interval type-2 multivariate Gaussian function is used in the hidden layer, whereas the nonlinear Chebyshev function is embedded in the output layer. The efficacy of eT2ELM is proven with four data streams possessing various concept drifts, comparisons with prominent classifiers, and statistical tests, where eT2ELM demonstrates the most encouraging learning performances in terms of accuracy and complexity