106,444 research outputs found
Alternative strategies for aggregating prices in the CPI
Consumer price indexes ; Prices
Alternative strategies for aggregating prices in the CPI - commentary
Consumer price indexes ; Prices
Comparison of group recommendation algorithms
In recent years recommender systems have become the common tool to handle the information overload problem of educational and informative web sites, content delivery systems, and online shops. Although most recommender systems make suggestions for individual users, in many circumstances the selected items (e.g., movies) are not intended for personal usage but rather for consumption in groups. This paper investigates how effective group recommendations for movies can be generated by combining the group members' preferences (as expressed by ratings) or by combining the group members' recommendations. These two grouping strategies, which convert traditional recommendation algorithms into group recommendation algorithms, are combined with five commonly used recommendation algorithms to calculate group recommendations for different group compositions. The group recommendations are not only assessed in terms of accuracy, but also in terms of other qualitative aspects that are important for users such as diversity, coverage, and serendipity. In addition, the paper discusses the influence of the size and composition of the group on the quality of the recommendations. The results show that the grouping strategy which produces the most accurate results depends on the algorithm that is used for generating individual recommendations. Therefore, the paper proposes a combination of grouping strategies which outperforms each individual strategy in terms of accuracy. Besides, the results show that the accuracy of the group recommendations increases as the similarity between members of the group increases. Also the diversity, coverage, and serendipity of the group recommendations are to a large extent dependent on the used grouping strategy and recommendation algorithm. Consequently for (commercial) group recommender systems, the grouping strategy and algorithm have to be chosen carefully in order to optimize the desired quality metrics of the group recommendations. The conclusions of this paper can be used as guidelines for this selection process
EFSIS: Ensemble Feature Selection Integrating Stability
Ensemble learning that can be used to combine the predictions from multiple
learners has been widely applied in pattern recognition, and has been reported
to be more robust and accurate than the individual learners. This ensemble
logic has recently also been more applied in feature selection. There are
basically two strategies for ensemble feature selection, namely data
perturbation and function perturbation. Data perturbation performs feature
selection on data subsets sampled from the original dataset and then selects
the features consistently ranked highly across those data subsets. This has
been found to improve both the stability of the selector and the prediction
accuracy for a classifier. Function perturbation frees the user from having to
decide on the most appropriate selector for any given situation and works by
aggregating multiple selectors. This has been found to maintain or improve
classification performance. Here we propose a framework, EFSIS, combining these
two strategies. Empirical results indicate that EFSIS gives both high
prediction accuracy and stability.Comment: 20 pages, 3 figure
Adaptive Online Prediction by Following the Perturbed Leader
When applying aggregating strategies to Prediction with Expert Advice, the
learning rate must be adaptively tuned. The natural choice of
sqrt(complexity/current loss) renders the analysis of Weighted Majority
derivatives quite complicated. In particular, for arbitrary weights there have
been no results proven so far. The analysis of the alternative "Follow the
Perturbed Leader" (FPL) algorithm from Kalai & Vempala (2003) (based on
Hannan's algorithm) is easier. We derive loss bounds for adaptive learning rate
and both finite expert classes with uniform weights and countable expert
classes with arbitrary weights. For the former setup, our loss bounds match the
best known results so far, while for the latter our results are new.Comment: 25 page
A literature review on the use of expert opinion in probabilistic risk analysis
Risk assessment is part of the decision making process in many fields of discipline, such as engineering, public health, environment, program management, regulatory policy, and finance. There has been considerable debate over the philosophical and methodological treatment of risk in the past few decades, ranging from its definition and classification to methods of its assessment. Probabilistic risk analysis (PRA) specifically deals with events represented by low probabilities of occurring with high levels of unfavorable consequences. Expert judgment is often a critical source of information in PRA, since empirical data on the variables of interest are rarely available. The author reviews the literature on the use of expert opinion in PRA, in particular on the approaches to eliciting and aggregating experts'assessments. The literature suggests that the methods by which expert opinions are collected and combined have a significant effect on the resulting estimates. The author discusses two types of approaches to eliciting and aggregating expert judgments-behavioral and mathematical approaches, with the emphasis on the latter. It is generally agreed that mathematical approaches tend to yield more accurate estimates than behavioral approaches. After a short description of behavioral approaches, the author discusses mathematical approaches in detail, presenting three aggregation models: non-Bayesian axiomatic models, Bayesian models, andpsychological scaling models. She also discusses issues of stochastic dependence.Health Monitoring&Evaluation,ICT Policy and Strategies,Public Health Promotion,Enterprise Development&Reform,Statistical&Mathematical Sciences,ICT Policy and Strategies,Health Monitoring&Evaluation,Statistical&Mathematical Sciences,Science Education,Scientific Research&Science Parks
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