We investigate a method for applying CBR to a source of data where there are no negative exemplars. Our problem domain is one of recommending characteristics of multidisciplinary collaborators based on a collection of funded grants. Thus, there are no negative exemplars. Lacking sufficient domain knowledge, we seek to apply a feedback algorithm to learn weights even in the absence of negative e...

This paper is a principal idea of case-based reasoning to feature weighting. The feature weighting method called CaDFeW (CAse-based Dynamic FEature Weighting) stores classification performance of randomly generated feature weight vectors. Also it retrieve similar feature weighting success story from the feature weighting case base and then designs a better feature weight vector dynamically for ...

The BioNLP ́09 Shared Task on Event Extraction presented an evaluation on the extraction of biological events related to genes/proteins from the literature. We propose a system that uses the case-based reasoning (CBR) machine learning approach for the extraction of the entities (events, sites and location). The mapping of the proteins in the texts to the previously extracted entities is carried ...

In real world applications, interested concepts are more likely to change rather than remain stable, which is known as concept drift. This situation causes problems on predictions for many learning algorithms including case-base reasoning (CBR). When learning under concept drift, a critical issue is to identify and determine “when” and “how” the concept changes. In this paper, we developed a co...

This paper introduces a proposal scheme for an Intelligent System applied to Pedagogical Advising using Case-Based Reasoning, to find consolidated solutions before used for the new problems, making easier the task of advising students to the pedagogical staff. We do intend, through this work, introduce the motivation behind the choices for this system structure, justifying the development of an...

We propose to significantly extend our work in EEG-based emotion detection for automated expressive performances of algorithmically composed music for affective communication and induction. This new system involves music composed and expressively performed in real-time to induce specific affective states, based on the detection of affective state in a human listener. Machine learning algorithms...

A very recent topic in CBR research deals with the automated optimisation of similarity measures—a core component of each CBR application—by using machine learning techniques. In our previous work, a number of approaches to bias and guide the learning process have been proposed aiming at more stable learning results and less susceptibility to overfitting. Those methods support the learner by in...

Selection hyper-heuristic methodologies explore the space of heuristics which in turn explore the space of candidate solutions for solving hard computational problems. This study investigates the performance of approaches based on a framework that hybridizes selection hyper-heuristics and population based incremental learning (PBIL), mixing offline and online learning mechanisms for solving dyn...

mechanism that dynamically changes the seeds, that is for deleting stored cases or adding new ones. That would provide a real incremental learning method with a capability to adapt to severe changes in the input space. Another improvement in the accuracy is expected when using a method for dynamically changing the punishment and reward parameters. An application of the proposed techniques is on...

Ensemble research has shown that the aggregated output of an ensemble of predictors can be more accurate than a single predictor. This is true also for lazy learning systems like Case-Based Reasoning (CBR) and k-NearestNeighbour. Aggregation is normally achieved by voting in classification tasks and by averaging in regression tasks. For CBR, this increased accuracy comes at the cost of interpre...