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In this paper we propose a new approach based on Symbolic Aggregate approximation (SAX), called improved iSAX to recognize efficient and accurate discovery of the important patterns, essential for time series data. The original SAX approach allows a very high-quality dimensionality reduction and distance measures to be defined on the symbolic approach and it is based on PAA (Piecewise Aggregate Approximation)...
The classification of imbalanced data is a well-studied topic in data mining. However, there is still a lack of understanding of the factors that make the problem difficult. In this work, we study the two main reasons that make the classification of imbalanced datasets complex: overlapping and data fracture. We present a Genetic Programming-based feature extraction method driven by Rough Set Theory...
The retransmission timeout (RTO) timer used in TCP has long been standardized by the IETF in RFC2988, referred to as the TCP-RFC in this paper. Over the years, various deficiencies have been identified. In this paper, we focus on the implicit RTO offset problem, where the exact timeout limit of each packet is stretched by restarting the timer using the current timer value on the arrival of each acknowledgement...
In business analysis, models are sometimes oversimplified. We pragmatically approach many problems with a single financial objective and include monetary values for non-monetary variables. We enforce constraints which may not be as strict in reality. Based on a case in distributed energy production, we illustrate how we can avoid simplification by modeling multiple objectives, solving it with an NSGA-II...
In this paper, we apply classification system denoted Belief Rough Set Classifier (BRSC) based on the hybridization of belief functions and rough sets to learn decision rules from uncertain data consisting of web usage. The uncertainty appears only in decision attributes and is handled by the Transferable Belief Model (TBM), one interpretation of the belief function theory. The web usage mining dataset...
Non-negative matrix factorization is an important method helpful in the analysis of high dimensional datasets. It has a number of applications including pattern recognition, data clustering, information retrieval or computer security. One its significant drawback lies in its computational complexity. In this paper, we introduce a new method allowing fast approximate transformation from input space...
Hyper-heuristics can be defined as search method for selecting or generating heuristics to solve difficult problem. A high level heuristic therefore operate on a set of low level heuristics with the overall aim of selecting the most suitable set of low level heuristics at a particular point in generating an overall solution. In this work, we propose a set of constructive hyper-heuristics for solving...
Attribute reduction is a basic issue in knowledge representation and data mining. It simplifies an information system by discarding some redundant attributes. In this paper, we present a hybrid approach that combines the nature of variable neighbourhood search in the first phase with an iterated local search in the second phase that always accepts best solutions. The approach is tested over 13 well-known...
This paper proposes a novel wavelet based nonlinear image enhancement algorithm, including dynamic range compression, contrast enhancement and color restoration, for recorded images in non-uniform and uniform-dark lightning conditions. Dynamic range compression (DRC) has been applied by nonlinear function for enhancing dark and reducing the intensity of bright regions. The intensity has been tuned...
Estimating the head pose plays an important role in computer vision and also as a key task for visual surveillance and face recognition applications hence a prominent problem in computer vision. Most of the works in this field suffer from lack of continuous estimating of the head pose and high accuracy. We know fuzzy systems as universal approximator capable of approximating an unknown function by...
The paper is devoted to intelligent matching ontological subgraphs to concepts. The problem is considered from the point of view of rough set theory. An accuracy of approximation determines how far in a semantic space it is from a given ontological subgraph to a given concept. The approach presented in the paper can be applied to intelligent searching of a bibliographical data base for abstracts semantically...
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...
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