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This paper, presents a novel identification approach using fuzzy neural networks. It focuses on structure and parameters uncertainties which have been widely explored in the literatures. The main contribution of this paper is that an integrated analytic framework is proposed for automated structure selection and parameter identification. A kernel smoothing technique is used to generate a model structure automatically in a fixed time interval. To cope with structural change, a hysteresis strategy is proposed to guarantee finite times switching and desired performance.

Decision-tree algorithms are known to be unstable: small variations in the training set can result in different trees and different predictions for the same validation examples. Both accuracy and stability can be improved by learning multiple models from bootstrap samples of training data, but the "meta-learner" approach makes the extracted knowledge hardly interpretable. In the following paper, we present the Info-Fuzzy Network (IFN), a novel information-theoretic method for building stable and comprehensible decision-tree models. The stability of the IFN algorithm is ensured by restricting the tree structure to using the same feature for all nodes of the same tree level and by the built-in statistical significance tests. The IFN method is shown empirically to produce more compact and stable models than the "meta-learner" techniques, while preserving a reasonable level of predictive accuracy.

Data mining is much more than simply building statistical models from large collections of data. In particular, this paper records a core task of mining as exploring through the space of models that are built in a data mining project. The idea was first introduced through the concept of multiple inductive learning (MIL) (Williams, 1988, 1991) and further developed in practice as mining the data mine (Williams and Huang, 1997). Many data mining advances that have since emerged have further developed the idea: multiple modelling, ensemble learning, bagging and boosting all help the data miner explore different ideas and look for different insights in modelling. In this paper we review these ideas and a number of data mining projects that highlight the significant role played by mining the data mine.

Machine learning has been studied intensively during the past two decades. This has often been motivated by the desire to automate the process of knowledge acquisition during the construction of expert systems. Machine learning has also gained attention recently in the context of data mining and knowledge discovery. A great deal of research on machine learning has focused on classification learning. This paper presents and evaluates several machine learning techniques for classification learning. Special attention is given to inductive learning, which is one of the most mature and widely used machine learning approaches currently available. Current trends and recent developments in machine learning research are also presented.