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Information monitoring is a very essential activity for management, control and decision making in various applications. This paper proposes an intelligent system for monitoring information sources that consists of many adaptive agents cooperate with each other in order to monitor every information item updates at the right time without over loading of system resources. The main key of the presented...
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...
In classifier combining, predictions of several classifiers are aggregated into a single prediction in order to improve the classification quality. Among others, fuzzy integrals are commonly used as aggregation operators. Usually, Sugeno lambda-measure is used as the fuzzy measure of the integral. However, interaction between the classifiers in the team (diversity), an important property in classifier...
Modeling distributed system and modeling intelligent system means that we create a model of a system with corresponding behavior. Multiagent systems are complex systems implementing a platform which is responsible for agent management including inter-agent communication, an agent specification, its behavior, etc. There are many agent platforms that are primarily intended for different applications...
In some machine learning applications using soft labels is more useful and informative than crisp labels. Soft labels indicate the degree of membership of the training data to the given classes. Often only a small number of labeled data is available while unlabeled data is abundant. Therefore, it is important to make use of unlabeled data. In this paper we propose an approach for Fuzzy-Input Fuzzy-Output...
Support Vector Machine (SVM) is one of the most popular tools for solving general classification and regression problems because of its high predicting accuracy. However, the training phase of nonlinear kernel based SVM algorithm is a computationally expensive task, especially for large datasets. In this paper, we propose an intelligent system to solve large classification problems based on parallel...
This work utilize Round Robin (RR) mechanism to systematically explore neighbors of solution. RR is one of the simplest scheduling algorithms, which assigns time slices to each process in equal portions and in circular order handling all processes without priority. In this work, we consider five different neighborhood structures. RR gives each neighborhood a certain number of iterations to explore...
In some cases the fitness value of a knowledge base is not completely determined, but just bounded in an interval. In this case the fitness value is modelled by a random variable. Thus the comparison of random variables allows to compare the fitness values when they are not completely determined. In this contribution we consider a quite new proposal in stochastic comparison: statistical preference...
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...
In this paper we present an automatic authority control system for raw noisy web data based on Data Mining. We use a hierarchical clustering approach with a special distance measure combination of three parameters: author name similarity, token similarity and co-authors similarity, each one defined in a specific way. A preliminary experimental study has been performed with real data obtained from...
This paper presents a methodology to find optimal solutions for linear programming problems on imprecise conditions. By using α-cuts, the cumulative membership function and the classic fuzzy linear programming model, a fuzzy joint parameters where its left hand side is defined by any kind of fuzzy set and its right hand side is defined by linear fuzzy sets, is solved and its crisp output is found...
Word Alignment is an important supporting task for different NLP applications like training of machine translation systems, translation lexicon induction, word sense discovery, word sense disambiguation, information extraction and the cross-lingual projection of linguistic information. In this paper we study the main rules and guidelines required to build an aligner tool for Arabic language which...
This paper proposes the use of a RuleML format rulebase using Reaction RuleML that can be used to support the development of automated web interface. Database metadata can be extracted from system catalogue tables in typical relational database systems, and used in conjunction with the rulebase to produce appropriate web form elements. First results show that this mechanism successfully insulates...
Unequal Area Facility Layout Problem (UA-FLP) has been addressed by several methods. However, UA-FLP has only been solved regarding quantitative criteria. Our approach includes subjective features to UA-FLP, which are difficult to take into account with a classical heuristic optimization. For that, an Interactive Genetic Algorithm (IGA) is proposed that allows an interaction between the algorithm...
This paper presents a model of a supervised machine learning approach for classification of a dataset. The model extracts a set of patterns common in a single class from the training dataset according to the rules of the pattern-based subspace clustering technique. These extracted patterns are used to classify the objects of that class in the testing dataset. The user-defined threshold dependence...
A system of Multiple Neural Networks has been proposed to solve the face recognition problem. Our idea is that a set of expert networks specialized to recognize specific parts of face are better than a single network. This is because a single network could no longer be able to correctly recognize the subject when some characteristics partially change. For this purpose we assume that each network has...
Fuzzy C-means (FCM) and Rough K-means (RKM) algorithms are two popular soft clustering algorithms that allow for overlapping clusters. The overlapping clusters can be useful in applications where restrictions imposed by crisp clustering that force assignment of every object to a unique cluster may not be practical. Likewise RKM and FCM, interval set representation of clusters would also generate overlapping...
Signature is a popular method of seeking approval and authentication between various parties in many transaction applications. Signature pattern recognition is done by processing a set of data that consists of (x, y) coordinates, representing online signature. Particle Swarm Optimisation technique is used to find and analyse the baseline feature that exists within the signature. Signatures were taken...
This paper presents a computational model of language generation, based on Phase Theory, that automatically constructs sentences from underlying numerations. This model incorporates explicit algorithms that determine selection and merger of Lexical Items from a subnumeration, determine the labels of Merged syntactic elements, account for movement of elements within a derivation, and account for when...
Statistically-based parsers for large corpora, in particular the Penn Tree Bank (PTB), typically have not used all the linguistic information encoded in the annotated trees on which they are trained. In particular, they have not in general used information that records the effects of derivations, such as empty categories and the representation of displaced phrases, as is the case with passive, topicalization,...
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