Titlebook: Research and Development in Intelligent Systems XXXI; Incorporating Applic Max Bramer,Miltos Petridis Conference proceedings 2014 Springer

健壮

Computationally Efficient Rule-Based Classification for Continuous Streaming Data these vast amounts of data as it is generated in real-time. Data stream classification is one of the most important DSM techniques allowing to classify previously unseen data instances. Different to traditional classifiers for static data, data stream classifiers need to adapt to concept changes (c

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Improved Stability of Feature Selection by Combining Instance and Feature Weightingess. Both types of weights are based on margin concepts and can therefore be naturally interlaced. We report experiments performed on both synthetic and real data (including microarray data) showing improvements in selection stability at a similar level of prediction performance.

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Towards a Parallel Computationally Efficient Approach to Scaling Up Data Stream Classificationing approaches. The research area of Data Stream Mining (DSM) is developing data mining algorithms that allow us to analyse these continuous streams of data in real-time. The creation and real-time adaption of classification models from data streams is one of the most challenging DSM tasks. Current

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Preference and Sentiment Guided Social Recommendations with Temporal Dynamicsome the accepted standard in the future. Social recommender systems that harness knowledge from user expertise and interactions to provide recommendation have great potential in capturing such trending information. In this paper, we model our recommender system using sentiment rich user generated pr

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Benchmarking Grammar-Based Genetic Programming AlgorithmsIn order for these variants to be compared, the community requires a rigorous means for benchmarking the algorithms. However, GP as a field is not known for the quality of its benchmarking, with many identified problems, making direct comparisons either difficult or impossible in many cases. Aside f

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The Effects of Bounding Rationality on the Performance and Learning of CHREST Agents in Tileworldn and recall huge numbers of board configurations to produce near-optimal actions provides evidence that . mechanisms are likely to underpin human learning. Cognitive theories based on chunking argue in favour for the notion of bounded rationality since relatively small chunks of information are lea

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