Recently, decision level fusion has shown great potential to increase classification accuracy beyond the level reached by individual classifiers. A considerable body of literature exists on identifying optimal ways to combine classifiers. However, the selection of the classifiers to be combined is equally, if not more, crucial if an improvement is to be made for certain classifier combination s...

The ensemble classification paradigm is an effective way to improve the performance and stability of individual predictors. Many ways to build ensembles have been proposed so far, most notably bagging and boosting based techniques. Evolutionary algorithms (EAs) also have been widely used to generate ensembles. In the context of heterogeneous ensembles EAs have been successfully used to adjust w...

Hybrid face recognition, using image (2D) and structural (3D) information, has explored the fusion of Nearest Neighbour classifiers. This paper examines the effectiveness of feature modelling for each individual modality, 2D and 3D. Furthermore, it is demonstrated that the fusion of feature modelling techniques for the 2D and 3D modalities yields performance improvements over the individual cla...

So far few theoretical works investigated the conditions under which specific fusion rules can work well, and a unifying framework for comparing rules of different complexity is clearly beyond the state of the art. A clear theoretical comparison is lacking even if one focuses on specific classes of combiners (e.g., linear combiners). In this paper, we theoretically compare simple and weighted a...

This paper presents various classifiers results from a system that can automatically recognize four different static human body postures in video sequences. The considered postures are standing, sitting, squatting, and lying. The three classifiers considered are a naı̈ve one and two based on the belief theory. The belief theory-based classifiers use either a classic or restricted plausibility cr...

Insect recognition is a hard problem because the difference of appearance between insects is so small that only some entomologist experts can distinguish them. Besides that, insects are often composed of several parts (multiple views) which generate more degrees of freedom. This chapter proposes several discriminative coding approaches and one decision fusion scheme of heterogeneous class sets ...

Problem of pattern recognition is accompanying our whole life, therefore methods of automatic pattern recognition is one of the main trend in Artificial Intelligence. Multiple classifier systems (MCSs) are currently the focus of intense research. In this conceptual approach, the main effort is concentrated on combining knowledge of the set of individual classifiers. Proposed work presents a bri...

This extended abstract describes the method for MIREX 2011 task “Query by Singing/Humming”. The method extracts the pitch values from a database, and then the extracted data are normalized by zero elimination, meanshifting, median filtering, average filtering, and min-max scaling [1-4]. For matching, we used multiple classifiers. From those, a number of distances are calculated and combined bas...

A multiple classifiers system which adopts an effective weighting policy to combine the output of several classifiers, generally leads to a better performance in image classification. The two most commonly used weighting policies are Bagging and Boosting algorithms. However, their performance is limited by high levels of ambiguity among classes. To overcome this difficulty, an adaptive threshol...