This paper presents and critically discusses an approach for knowledge modelling and reasoning in a system for monitoring and coaching of senior adults. We present a modular architecture of the system and a detailed description of the modelling methodology which originates from the field of multi-criteria decision modelling and differs from the commonly used ones in this problem domain. The methodology has several characteristics that make it fit well to the purpose in this application and initial insights from potential users are positive. A discussion of the suitability of the proposed methodology for knowledge representation and reasoning in the given problem domain is provided, with an outline of its potential benefits and drawbacks and a comparison with the ontological approach.

Methods for learning decision rules are being successfully applied to many problem domains, especially where understanding and interpretation of the learned model is necessary. In many real life problems, we would like to predict multiple related (nominal or numeric) target attributes simultaneously. Methods for learning rules that predict multiple targets at once already exist, but are unfortunately based on the covering algorithm, which is not very well suited for regression problems. A better solution for regression problems may be a rule ensemble approach that transcribes an ensemble of decision trees into a large collection of rules. An optimization procedure is then used for selecting the best (and much smaller) subset of these rules, and to determine their weights. Using the rule ensembles approach we have developed a new system for learning rule ensembles for multi-target regression problems. The newly developed method was extensively evaluated and the results show that the accuracy of multi-target regression rule ensembles is better than the accuracy of multi-target regression trees, but somewhat worse than the accuracy of multi-target random forests. The rules are significantly more concise than random forests, and it is also possible to create very small rule sets that are still comparable in accuracy to single regression trees.

Meta decision trees (MDTs) are a method for combining multiple classifiers. We present an integration of the algorithm MLC4.5 for learning MDTs into the Weka data mining suite. We compare classifier ensembles combined with MDTs to bagged and boosted decision trees, and to classifier ensembles combined with other methods: voting and stacking with three different meta-level classifiers (ordinary decision trees, naive Bayes, and multi-response linear regression - MLR). Meta decision trees. Techniques for combining predictions obtained from multiple base-level classifiers can be clustered in three combining frameworks: voting (used in bagging and boosting), stacked generalization or stacking [7] and cascading. Meta decision trees (MDTs) [5] adopt the stacking framework of combining base-level classifiers. The difference between meta and ordinary decision trees (ODTs) is that MDT leaves specify which base-level classifier should be used, instead of predicting the class value directly. Th...