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In the paper the novel feature selection method, using wrapper model and ensemble approach, is presented. In the proposed method features are selected dynamically, i.e. separately for each classified object. First, a set of identical one-feature classifiers using different single feature is created and next the ensemble of features (classifiers) is selected as a solution of optimization problem using...
In this paper the issue of multi-label data stream classification was addressed. To deal with the posed problem, we introduced a recognition system that is build upon a two level architecture. The first level is a Binary Relevance multi-label classifier, and the second is a correction procedure that employs competence and cross-competence measures to adjust the output of the Binary Relevance classifier...
When it comes to the use of any recognition systems in the real world environment, it turns out that the reality differs from the theory. There is an assumption that the distribution of the incoming data will be at least similar to the distribution of the data, which were used during the learning process and that learning dataset represents the entire space of the problem. In fact, the incoming data...
The paper presents an advanced method of recognition of patient's intention to move hand prosthesis during the grasping and manipulation of objects in a dexterous manner. The proposed method is based on recognition of electromiographic (EMG) and mechanomiographic (MMG) bio signals using two-stage hierarchical multiclassifier system (MCS) with dynamic ensemble selection scheme (DES) and probabilistic...
The concept of classifier competence in the feature space is fundamental to dynamic classifier selection in multiple classifier systems (MCS). Competence function (measure) of base classifier can be determined using validation set in the two step procedure. The first step consists in creating competence set, i.e. the set of classifier competences for all validation objects. To this end a hypothetical...
The paper deals with an enhanced approach of recognising intentions of a patient to move a hand prosthesis when manipulating and grasping items in a way that is skillful. The method follows a 2-level multi-classifier system (MCS) with heterogeneous classified bases with a relationship to EMG and MMG signals and a mechanism that combines the use of a probabilistic competence functions of base classifiers...
In the paper the problem of EMG-based recognition of user intent for the control of bio-prosthetic hand is addressed. The multiple classifier systems (MCS) with dynamic ensemble selection (DES) strategy based on the original concept of competence measure are applied. In the proposed method first a probabilistic reference classifier (RRC) is constructed which - on average - acts like the classifier...
This paper presents a measure of competence based on a randomized reference classifier (RRC) for classifier ensembles. The RRC can be used to model, in terms of class supports, any classifier in the ensemble. The competence of a modelled classifier is calculated as the probability of correct classification of the respective RRC. A multiple classifier system (MCS) was developed and its performance...
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