Abstract. RISE is a well-known multi-strategy learning algorithm that combines rule induction and instance-based learning. It achieves higher accuracy than some state-of-the-art learning algorithms, but for large data sets it has a very high average running time. This work presents the analysis and experimental evaluation of SUNRISE, a new multi-strategy learning algorithm based on RISE, developed to be faster than RISE with similar accuracy.
The SUNRISE AlgorithmRISE (Rule Induction from a Set of Exemplars) [2] induces all the rules together. If a generalization of a rule has positive or null effect on the global accuracy, the change is kept. The RISE algorithm is presented in Table 1. SUNRISE tries to generalize the rules more than once before including them in the rule set and only accepts the changes if the effect on the global accuracy is strictly positive; i.e., a new rule is only added to the rule set if the set achieves a higher accuracy than before its inclusion. The SUNRISE algorithm is presented in Table 2. The fact that SUNRISE does not use Occam's Razor increases the algorithm's speed because it increases the probability of no modification in the rule set after an iteration of the outermost loop Repeat, thus causing the algorithm's stop. Only after k generalizations a rule is evaluated to determine if it must or not belong to the rule set. It makes SUNRISE faster than RISE, since the latter evaluates each generalization made in each rule. The value k is a parameter of the SUNRISE algorithm whose value has to be experimentally determined.
Experimental EvaluationIn the experiments, 22 data sets [1] were used to compare the performance of the new algorithm, SUNRISE, to that of the RISE algorithm. The test method used in this research was the paired t test with n-fold cross-validation [5]. To adjust the parameter k, an internal cross-validation was made [5]. The value for k that achieved better performance in most of the data sets was k ≤ 3. All tests were carried through in a Pentium III 450MHz computer with 64MBytes RAM. Table 3 presents the running time (training and testing) of each algorithm for each one of the data sets. The two last columns show the results obtained by the SUNRISE
The Sigma-Point Kalman Filters (SPKF) is a family of filters that achieve very good performance when applied to time series. Currently most researches involving time series forecasting use the Sigma-Point Kalman Filters, however they do not use an ensemble of them, which could achieve a better performance. The REC analysis is a powerful technique for visualization and comparison of regression models. The objective of this work is to advocate the use of REC curves in order to compare the SPKF and ensembles of them and select the best model to be used.
The development of new technologies in urban automation has increasingly intensified in recent decades. Among the research initiatives, there is the study and design of service robots for home tasks, such as: cleaning floors, windows, and pools. The technology used in these robots depends on specific resources and great investments, and consequently, the price is expensive, restricting the use for people with high purchasing power. Thus, it is important to study means to manufacture robots with better cost-benefit, looking for to develop mechanisms that can reduce the production costs, and then a greater number of people can use such technology. Therefore, the objective of this work is to develop an underwater cleaning robot, which can be used for cleaning pools. Several aspects of the project, a model created by means of computational methods, and some analyses of the robot structure will be presented.
The chapter describes the stages of an autonomous mobile robot project, in this case, an underwater cleaning robot. First, the authors analyze the products already available for costumers, mainly focusing on the tasks they can perform (instead of the systems they use), in order to define the requirements of their project. Then, they build some models, based in the literature available. Based on them, the authors dimension the parts and systems by evaluating the results of these models. Finally, the authors use all information gathered to create a prototype, modeled with a CAE system.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.