A comparative analysis of methods for pruning decision trees

In this paper, we address the problem of retrospectively pruning decision trees induced from data, according to a top-down approach. This problem has received considerable attention in the areas of pattern recognition and machine learning, and many distinct methods have been proposed in literature. We make a comparative study of six well-known pruning methods with the aim of understanding their theoretical foundations, their computational complexity, and the strengths and weaknesses of their formulation. Comments on the characteristics of each method are empirically supported. In particular, a wide experimentation performed on several data sets leads us to opposite conclusions on the predictive accuracy of simplified trees from some drawn in the literature. We attribute this divergence to differences in experimental designs. Finally, we prove and make use of a property of the reduced error pruning method to obtain an objective evaluation of the tendency to overprune/underprune observed in each method     

Process optimization via confidence region: a case study from micro-injection molding

Journal of Intelligent Manufacturing - In industrial research, experiments are designed to determine the optimal factor levels of the process parameters. Typically, experimental data are used to...     

An empirical evaluation of active learning strategies for profile elicitation in a conversational recommender system

Journal of Intelligent Information Systems - Conversational Recommender Systems have received widespread attention in both research and practice. They assist people in finding relevant and...     

Using recurrent neural networks to detect changes in autocorrelated processes for quality monitoring

With the growing of automation in manufacturing, process quality characteristics are being measured at higher rates and data are more likely to be autocorrelated. A widely used approach for statistical process monitoring in the case of autocorrelated data is the residual chart. This chart requires that a suitable model has been identified for the time series of process observations before residuals can be obtained. In this work, a new neural-based procedure, which is alleviated from the need for building a time series model, is introduced for quality control in the case of serially correlated data. In particular, the Elman’s recurrent neural network is proposed for manufacturing process quality control. Performance comparisons between the neural-based algorithm and several control charts are also presented in the paper in order to validate the approach. Different magnitudes of the process mean shift, under the presence of various levels of autocorrelation, are considered. The simulation results indicate that the neural-based procedure may perform better than other control charting schemes in several instances for both small and large shifts. Given the simplicity of the proposed neural network and its adaptability, this approach is proved from simulation experiments to be a feasible alternative for quality monitoring in the case of autocorrelated process data.     

Content-based and collaborative techniques for tag recommendation: an empirical evaluation

The rapid growth of the so-called Web 2.0 has changed the surfers’ behavior. A new democratic vision emerged, in which users can actively contribute to the evolution of the Web by producing new content or enriching the existing one with user generated metadata. In this context the use of tags, keywords freely chosen by users for describing and organizing resources, spread as a model for browsing and retrieving web contents. The success of that collaborative model is justified by two factors: firstly, information is organized in a way that closely reflects the users’ mental model; secondly, the absence of a controlled vocabulary reduces the users’ learning curve and allows the use of evolving vocabularies. Since tags are handled in a purely syntactical way, annotations provided by users generate a very sparse and noisy tag space that limits the effectiveness for complex tasks. Consequently, tag recommenders, with their ability of providing users with the most suitable tags for the resources to be annotated, recently emerged as a way of speeding up the process of tag convergence. The contribution of this work is a tag recommender system implementing both a collaborative and a content-based recommendation technique. The former exploits the user and community tagging behavior for producing recommendations, while the latter exploits some heuristics to extract tags directly from the textual content of resources. Results of experiments carried out on a dataset gathered from Bibsonomy show that hybrid recommendation strategies can outperform single ones and the way of combining them matters for obtaining more accurate results.     

Analysis of the behaviour of a yield-control support system in squeezing rock

Recent innovations in yield-control support systems allow to increase the rate of advance when tunnelling in difficult conditions is associated with severely squeezing rock. Such systems which imply the insertion in the lining of highly deformable concrete elements are being adopted successfully in tunnelling projects using conventional excavation methods. The Saint Martin access adit excavated in a Carboniferous Formation along the Base Tunnel of the Lyon-Turin rail line is presented as a case study. Numerical analyses are discussed to compare the results of computed and measured performance of a typical monitored section and to find out possible optimizations of the support system adopted.     

User profiling and virtual agents: a case study on e-commerce services

The main contribution of this work is the design of an application framework based on both conversational agents and user profiling technologies for the development of e-commerce services. User profiles are exploited by conversational agents to help customers in retrieving potentially interesting products from a catalogue. Three techniques were used for collecting data for a usability test: eye-movement tracking, questionnaire, and recording the user-system dialogue. The main outcomes of the experimental sessions are: (1) the dialogue capabilities of the agent facilitate the interaction between the user and the e-commerce site; and, (2) user profiles improve the retrieval capabilities of the agent. Finally, some limitations of the user profiling techniques adopted in the framework are discussed and a more sophisticated content-based profiling technique is proposed.     

A Framework for Using Web Services to Enhance QoS for Content Delivery

An integrated approach to quality of service for content delivery using Web services includes quality definitions, user contracts, and fault monitoring.