Ensembles of classifiers based on dimensionality reduction
We present a novel approach for the construction of ensemble classifiers based on dimensionality reduction. The ensemble members are trained based on dimension-reduced versions of the training set. In order to classify a test sample, it is first ...
Instance reduction for time series classification using MDL principle
Research in time series classification has shown that the one nearest neighbor with Dynamic Time Warping measure in most cases outperforms more advanced classification algorithms. Instance reduction is one of the approaches to improve time and ...
Combining diverse one-class classifiers by means of dynamic weighted average for multi-class pattern classification
One-Class Classifier (OCC) has been widely used for its ability to learn without counterexamples. Its extension for multi-class implementation offers an open scheme which allows easily adding new classes. However, using OCCs for the multi-class ...
Active seed selection for constrained clustering
Active learning for semi-supervised clustering allows algorithms to solicit a domain expert to provide side information as instances constraints, for example a set of labeled instances called seeds. The problem consists in selecting the queries to ...
Fuzzy c-Least Medians clustering for discovery of web access patterns from web user sessions data
Mining web usage data of e-business organizations is essential to provide knowledge about clients’ web utilization patterns, which can help these businesses in landing at vital business choices. Because of non-deterministic web access behavior of ...
Scalable data-driven modeling of spatio-temporal systems: Weather forecasting
In this paper, a new data-driven method for short-range forecasting of spatio-temporal systems is proposed. It uses NCEP data as raw data to construct forecasting model. The global model consists of several local models. Each local model is ...
An effective method for approximate representation of frequent itemsets
In data mining, finding frequent itemsets is a critical step to discovering association rules. The number of frequent itemsets may, however, be huge if the threshold of minimum support is set at a low value or the number of items in the ...
Financial distress prediction using SVM ensemble based on earnings manipulation and fuzzy integral
Financial distress prediction (FDP) has received considerable attention from both practitioners and researchers. This paper proposes a novel support vector machine (SVM) classifier ensemble framework based on earnings manipulation and fuzzy ...
An association rules based method for classifying product offers from e-shopping
Price comparison services are widely used by e-shopping customers. Such e-shopping sites receive product offers from thousands of online stores, and in order to provide price comparison, product categorization, and searching, it is necessary to ...
A feature selection method based on a neighborhood approach for contending with functional and anatomical variability in fMRI group analysis of cognitive states
The study of cognitive processes performed by the human brain has greatly benefited from new technologies able to infer neuronal activity by means of noninvasive methods. This is the case of functional magnetic resonance imaging. Digital image ...
Deceptive text detection using continuous semantic space models
We identify deceptive text by using different kinds of features: A continuous semantic space model based on latent Dirichlet allocation topics (LDA), one-hot representation (OHR), syntactic information from syntactic n-grams (SN), and lexicon-...
Forecasting, clustering and patrolling criminal activities
Tools that perform pattern recognition analysis of crimes, comprising at the same time forecasting, clustering, and recommendations on real data such as patrolling routes, are not fully integrated; modules are developed separately, and thus, a ...
A crowdsourcing approach for personalization in human activities recognition
The technology trend of context-aware computer systems carries the promise of more flexible automated systems, with a high degree of adaptation to the user’s situation, but it implies as a precondition that the context information (such as the ...
Impulsive noise filtering using a Median Redescending M-Estimator
Salt and Pepper noise removal is an important image preprocessing task, it has two simultaneous demands: the suppression of impulses and the preservation of edges. To address this problem in gray scale images, we propose an efficient method which ...
