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The concept of similarity between objects has traditionally been taken as the criterion for recognising their membership of a given class. This paper considers how well an object fits into a class by using the concept of adequacy introduced by the LAMDA learning system [6],[9]. The Global Adequacy Degree (GAD) is a function of the object’s class membership. An adequacy threshold is associated with...
The article discusses the development of thematic ontology for use in the educational process. We propose a method for the automated thesauri development using the web ontology editor "OntoMASTER-ontology".
In order to exploit the massive image information and to handle overload, techniques for analysing image content to facilitate indexing and retrieval of images have emerged. In this paper, a semantic image content analysis framework based on Fuzzy Petri Net is presented. Knowledge scheme is used to define more general and complex semantic concepts and their relations in the context of the examined...
This paper compares two representations of text within the same experimental setting for sentiment orientation analysis, and in particular focuses on the sensitivity of the analysis to sentence length. The two representations compared in this paper are bag-of-words (BoW) and nine dimensional vector (9Dim). The former represents text with a high dimensional feature vector, which ignores grammatical...
In the paper the study of knowledge hierarchical representation for automated reasoning is presented. The hierarchical knowledge representation is proposed for predictive modeling purpose. It is improved an effective automated reasoning structure for data set analyzes and making decisions based on complex relations between this data. It is important to emphasize that it is not considered a - priori...
Concept lattice is accurate and complete in knowledge representation and is an effective tool for data analysis and knowledge discovery. This paper focuses on incremental computation of intent reduction of concepts. By theoretical analysis of characteristic change of intent reduction of lattice nodes during incremental construction of concept lattice, it advances an incremental algorithm to compute...
In order to solve complex knowledge reduction, the relative conditional partition granularity and new knowledge significance, quantitative representations for the relative classification ability of decision attributes are defined in this paper. And new knowledge partition granularity and new relative conditional partition granularity are constructed to transform inconsistent decision tables into "consistent"...
By the combination of feature and concept hierarchy model and the definition of innovation about concept, the method of structured processing and knowledge hierarchical representation for injection mould repair schemes is put forward under the condition of non-fuzzy or fuzzy data. Rule sets can be provided by knowledge induction for injection mould repairs based on basic rough set, but the rule sets...
Battle Management Language emerges to enhance the interoperability among M&S system, C4I system and future robotic forces, by applying an unambiguous, communicable, comprehensive language and data-exchange mechanism to digitize the information transferred within military systems. Both SISO and NATO have noticed its importance and started their research partly cooperatively. Most researchers identify...
Concept lattice is accurate and complete in knowledge representation and is an effective tool for data analysis and knowledge discovery. This paper focuses on classification rules mining based on concept lattices. By modifying incremental computation of intent reduction of concepts, it develops a method to mine affirmative classification rules and approximate classification rules using intent reduction.
This paper deals with the issue of gradual classification of a multivariate sequence where the number of candidate time-series generators is significantly high. It proposes a prediction scheme that consists of two components: a hierarchical structure which organizes the time-series models and a decision maker tool that assigns and evolves a respective hierarchy of probabilities; the latter expresses...
Nowadays, massive amounts of data that are often geographically distributed and owned by different organisations are being mined. As consequence, a large mount of knowledge is being produced. This causes the problem of efficient knowledge management and mining. The main aim is to develop DM infrastructures to fully exploit the benefit of the knowledge contained in these very large data repositories...
A collaborative emergency call taking information system in the Czech Republic processes calls from the European 112 emergency number. Large amounts of various incident records are stored in its databases. The data can be used for mining spatial and temporal anomalies. When such an anomalous situation is detected so that the system could suffer from local or temporal performance decrease, either a...
The algorithm on Decision tree is the most widely used method of inductive inference, and it is a simple method of knowledge representation, Different examples can be divided into representative categories, such as classifier and prediction models. This article introduces the basic concepts of classifier, the principle of decision tree and algorithm ID3, analyses the algorithm C4.5 and gives further...
In this paper, we propose an ontology for representing the prior knowledge related to video event analysis. It is composed of two types of knowledge related to the application domain and the analysis system. Domain knowledge involves all the high level semantic concepts in the context of each examined domain (objects, events, context...) whilst system knowledge involves the capabilities of the analysis...
Concept lattice is an effective formal tool for data analysis and knowledge extraction. Constrained concept lattice is a new concept lattice structure which uses predicate logic to describe the user's interesting background knowledge and merges the background knowledge into the process of concept lattice construction, so that the time and storage complexity of constructing concept lattice is reduced,...
Spatial data mining is a highly demanding field because very large amounts of spatial data have been collected in various applications, ranging from remote sensing (RS), to geographical information system (GIS), computer cartography, environmental assessment and planning, etc. Classification is a data mining technique where the data stored in a database is analyzed in order to find rules that describe...
Credit risk analysis is an important topic in the financial risk management. Due to recent financial crises, credit risk analysis has been the major focus of financial and banking industry. An accurate estimation of credit risk could be transformed into a more efficient use of economic capital. Existing models for estimating credit risk are not semantics-based. The objective of this work was to design...
As an important task of multi-relational data mining, multi-relational classification can directly look for patterns that involve multiple relations from a relational database and have more advantages than propositional data mining approaches. According to the differences in knowledge representation and strategy, the paper researched three kind of multi-relational classification approaches that are...
For reasoning with uncertain knowledge causal semantic analysis is proposed to construct logical rules,which are extracted from decision tree induction and Bayes inference based on generalized information theory. These rules can represent multi-level semantic knowledge of the relationship between the data and information implicated. Empirical studies on a set of natural domains show that the semantic...
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