Towards Comprehensive Foundations of Computational Intelligence

Abstract. Although computational intelligence (CI) covers a vast variety of different methods it still lacks an integrative theory. Several proposals for CI foundations are discussed: computing and cognition as compression, meta-learning as search in the space of data models, (dis)similarity based methods providing a framework for such meta-learning, and a more general approach based on chains of transformations. Many useful transformations that extract information from features are discussed. Heterogeneous adaptive systems are presented as particular example of transformation-based systems, and the goal of learning is redefined to facilitate creation of simpler data models. The need to understand data structures leads to techniques for logical and prototype-based rule extraction, and to generation of multiple alternative models, while the need to increase predictive power of adaptive models leads to committees of competent models. Learning from partial observations is a natural extension towards reasoning based on perceptions, and an approach to intuitive solving of such problems is presented. Throughout the paper neurocognitive inspirations are frequently used and are especially important in modeling of the higher cognitive functions. Promising directions such as liquid and laminar computing are identified and many open problems presented.

A Strategy for SPN Detection Based on Biomimetic Pattern Recognition and Knowledge-Based Features

The 22nd International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems (IEA-AIE 2009), Tainan, Taiwan ROC, 24-27 June 2009Image processing techniques have proved to be effective in improving the diagnosis of lung nodules. In this paper, we present a strategy for solitary pulmonary nodules (SPN) detection using radiology knowledge-based feature extraction scheme and biomimetic pattern recognition (BPR). The proposed feature extraction scheme intends to synthesize comprehensive information of SPN according to radiology knowledge, e.g. grey level features, morphological, texture and spatial context features. Using support vector machine (SVM), Naive Bayes (NB) and BPR as the classifiers to evaluate different feature representation schemes, our experimental study shows that the proposed radiology knowledge-based features can significantly improve the classification effectiveness of SPN detection from nonnodules, in terms of accuracy and F1 value, regardless of the classifiers used. We also note that BPR can deliver a consistent performance using our knowledge-based features, even the ratios between nonnodules and nodules are quite different in the training set.Department of Industrial and Systems EngineeringRefereed conference pape