Guest editorial: Special issue on advances in intelligent data processing and analysis

Abstract

In today’s information era, data (especially digital data) are generated at an unprecedented rate in terms of volume and velocity. Data gathered from the real world are often multifaceted, e.g. spatiotemporal data, multivariate data, and multimodal data, and exist in a variety of formats, e.g. structured/unstructured text, time-/frequency-domain signals, static images, and dynamic video streams. The available data samples also need to be processed carefully, e.g. cleansing, filtering, and transforming, before useful information can be extracted for inferring conclusions and supporting decision-making. Intelligent techniques stemming from the domain of soft computing, which include neural, fuzzy, and evolutionary computing methodologies and other related techniques, provide a viable approach to either automatically or semiautomatically process and analyse a large volume of data. Neural computing techniques are useful for processing low-level data samples and extracting information out from data samples, while fuzzy computing techniques are beneficial for analysing high-level human linguistic variables and performing inference/reasoning with if–then rules elicited from human experts. On the other hand, evolutionary computing techniques are capable of searching in a high-dimensional space and finding solutions for optimization problems. In this special issue, a total of nine of articles reporting recent research findings in intelligent data processing and analysis from either theoretical or practical perspectives are presented. It should be noted that these articles represent only a small fraction of researches in this fast-moving domain. We aspire that this special issue could provide useful insight and stir further research in the area of intelligent data processing and analysis. A summary of each article is as follows. In Knauer et al., an ensemble of Radial Basis Function (RBF) neural networks with c-divergence-based similarity measures is first formulated. Fusion of the RBF outputs with decision trees is then performed. In addition, a selection scheme for subsets of RBF networks based on their relevance in the fusion process is proposed. A number of hyperspectral imaging data sets are deployed for evaluation. Different tree-based learners and combination strategies, which include AdaBoost with decision trees, random forests, and pruned decision trees, are experimented. The results demonstrate the usefulness of the proposed fusion tree approach to combining multiple RBF outputs in deriving an accurate classification system. The use of Genetic Algorithm (GA) to design an appropriate structure of the Fuzzy ARTMAP (FAM) neural network is described in Loo et al. The network parameters and the order of training data are first determined by a GA. Then, an ensemble of FAM networks is optimized with another GA to improve its performance in undertaking data classification problems. Probabilistic voting is employed to combine the predictions from multiple FAM networks. The usefulness of the proposed model is demonstrated using benchmark problems from the UCI Machine Learning repository. To reduce patient waiting time and to improve quality of healthcare, Wang et al. adopt the Fuzzy Min–Max (FMM) C. P. Lim Deakin University, Waurn Ponds, Victoria, Australia