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A genetic algorithm is combined with two variants of the modularity (Q) network analysis metric to examine a substantial amount fisheries catch data. The data set produces one of the largest networks evaluated to date by genetic algorithms applied to network community analysis. Rather than using GA to decide community structure that simply maximizes modularity of a network, as is typical, we use two...
There are numerous problems of increasing significance where a pattern can have several classes simultaneously associated. This kind of problems, usually called multi-label problems, should be tackled with specific techniques in order to generate models more accurate than those obtained with classical classification algorithms. This work presents the adaptation of the J48 algorithm to multi-label...
Place recognition is a vital methodology for modeling environments and localizing autonomous mobile robots topologically. It can also be integrated in a hierarchical framework where it guides a fast and more precise metric position estimation. Especially for those hierarchical frameworks, it is crucial that the place recognition modules be highly accurate. In this paper, an information-theoretic approach...
Classification in imbalanced domains has become one of the most relevant problems within the area of Machine Learning at the present. This problem has raised in significance due to its presence in many real applications and it occurs when the distribution of the available examples to carry out the learning process is very different between the classes (often for binary class data-sets). Usually, the...
The liver is a common site for the occurrence of tumors. Automatic hepatic lesion segmentation is a crucial step for diagnosis and surgery planning. This paper presents a new fully automatic technique to segment the tumors in liver structure with no interaction from user. Contrast enhancement is applied to the slices of segmented liver, then adding each image to itself to have a white image with some...
Increasing use of computerized systems in our daily lives creates new adversarial opportunities for which complex mechanisms are exploited to mend the rapid development of new attacks. Behavioral Biometrics appear as one of the promising response to these attacks. But it is a relatively new research area, specific frameworks for evaluation and development of behavioral biometrics solutions could not...
Dimensionality reduction and feature selection in particular are known to be of a great help for making supervised learning more effective and efficient. Many different feature selection techniques have been proposed for the traditional settings, where each instance is expected to have a label. In multiple instance learning (MIL) each example or bag consists of a variable set of instances, and the...
This paper regards a group decision-making process, where experts' estimates are expressed by triangular fuzzy numbers (TFNs). It presents an approach for determination of the degree of coordination, the closeness of these opinions. The implementation of the idea is based on the metric approach providing an easy procedure to determine the coordination degree of experts' opinions. A concept of the...
This paper proposes an improved Hierarchical Multi-label Classification (HMC) method for solving the gene function prediction. The HMC task is transferred into a series of binary SVM classification tasks. By introducing the hierarchy constraint into learning procedures, two measures with incorporating prior information are implemented to improve the HMC performance. Firstly, for imbalanced functional...
Relevance feedback (RFB) involves requesting some user judgments for an initial set of search results and then using these judgments to improve search results. Typical queries may have multiple possible interpretations or facets, only one of which is relevant to a user's need, but top search results may be dominated by one interpretation or facet. Thus, if the user is only given the top results to...
This paper presents the methodology how to utilize sensor networks in order to predict human's thermal comfort and sensation. The neural network was dynamically organized on the basis of correlations with the thermal sensation of the occupants and many other values in the sensor network, and the structure of the neural network was updated cyclically. In this paper, the air-conditioning system in an...
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