Multielectrode recording techniques were used to record ensemble activity from 10 to 16 simultaneously active CA1 and CA3 neurons in the rat hippocampus during performance of a spatial delayed-nonmatch-to-sample task. Extracted sources of variance were used to assess the nature of two different types of errors that accounted for 30% of total trials. The two types of errors included ensemble "mi...

Decision tree (DT) is one of the most popular classification algorithms in data mining and machine learning. However, the performance of DT based credit scoring model is often relatively poorer than other techniques. This is mainly due to two reasons: DT is easily affected by (1) the noise data and (2) the redundant attributes of data under the circumstance of credit scoring. In this study, we ...

Species distribution models are widely used for stream bioassessment, estimating changes in habitat suitability and identifying conservation priorities. We tested the accuracy of three modelling strategies (single species ensemble, multi-species response and community classification models) to predict fish assemblages at reference stream segments in coastal subtropical Australia. We aimed to ev...

differential evolution Iztok Fister Jr.,∗ Iztok Fister,† and Janez Brest‡ Abstract Differential evolution possesses a multitude of various strategies for generating new trial solutions. Unfortunately, the best strategy is not known in advance. Moreover, this strategy usually depends on the problem to be solved. This paper suggests using various regression methods (like random forest, extremely ...

Optimization in dynamic environments is important in real-world applications, which requires the optimization algorithms to be able to find and track the changing optimum efficiently over time. Among various algorithms for dynamic optimization, particle swarm optimization algorithms (PSOs) are attracting more and more attentions in recent years, due to their ability of keeping good balance betw...

A solution to the multi-class classification problem is proposed founded on the concept of an ensemble of classifiers arranged in a hierarchical binary tree formation. An issue with this solution is that if a miss-classification occurs early on in the process (near the start of the hierarchy) there is no possibility of rectifying this error later on in the process. To address this issue a multi...

Dynamic optimization and multi-objective optimization have separately gained increasing attention from the research community during the last decade. However, few studies have been reported on dynamic multi-objective optimization (dMO) and scarce effective dMO methods have been proposed. In this paper, we fulfill these gabs by developing new dMO test problems and new effective dMO algorithm. In...

We study an ensemble of individuals playing the two games of the so-called Parrondo paradox. In our study, players are allowed to choose the game to be played by the whole ensemble in each turn. The choice cannot conform to the preferences of all the players and, consequently, they face a simple frustration phenomenon that requires some strategy to make a collective decision. We consider severa...

Ensemble pruning is crucial for the considerations of both efficiency and predictive accuracy of an ensemble system. This paper proposes a new Competitive measure for Ensemble Pruning based on Cross-Validation technique (CEPCV). Firstly, the data to be learnt by neural computing models are mostly drifting with time and environment, while the proposed CEPCV method can realize on-line ensemble pr...