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The paper exposes the behavior of the Decision Trees (DT) algorithms on a big database with many cases and many attributes: Forest Covertype (FC) from UCI Knowledge Discovery in Databases Archive. In classification experiments considered have been taken into account 22 splitting criteria and two pruning methods whose performances were presented in terms of classification error rate on test data, data...
This study presents the use of two different methods for the automatic prediction of the onset of paroxysmal atrial fibrillation (PAF) by means of surface electrocardiographic (ECG) signal. The first method is commonly used and consists in the analysis of the heart rate variability (HRV) of the ECG signal. Two significant parameters are taken into consideration: the time domain metric standard deviation...
The student's assessment is the core of learning process, which facilitates teachers to evaluate a student's knowledge level; furthermore, the precise measurement helps the students knowledge development reaches their full potential. Usually, this assessment method is also known as computer adaptive testing (CAT). The conventional CAT systems contain its own item bank, which is stored separately in...
The sparse representation-based classifier (SRC) has been developed and shows great potential for pattern classification. This paper aims to gain a discriminative projection such that SRC achieves the optimum performance in the projected pattern space. We use the decision rule of SRC to steer the design of a dimensionality reduction method, which is coined the sparse representation classifier steered...
This In this paper, a rough sets based analyzing system for Pseudorandom Number Generator (PRNG) is proposed to analyze the quality of the pseudorandom number generators. The strength of the cryptosystem relied on the quality of PRNGs. In particular, their outputs must be unpredictable in the absence of knowledge of the inputs and the input can not be guessed. On the other hand, the advance in computer...
The main goal of our research was to compile new methodology for building simplified learning models in a form of quasi-optimal sets of decision rules. The source informational database was extended by application of constructive induction to get a new, additional descriptive attribute, and then sets of decision rules were developed for source and extended database, respectively. In the last step,...
Atrial fibrillation (AF) is an arrhythmic behaviour of the heart, which occurs when the myocardium of the atrial chambers enter into a sustained chaotic and fractionated muscular contraction dynamic. Reliable detection of AF episodes in ECG monitoring devices, is important for early treatment and health risks reduction. A decision rule for identifying AF arrhythmic patterns was derived from RR-intervals...
The important issue in multi-class classification on support vector machines is the decision rule, which determines whether an input pattern belongs to a predicted class. To enhance the accuracy of multi-class classification, this study proposes a multi-weighted majority voting algorithm of support vector machine (SVM), and applies it to overcome complex facial security application. The proposed algorithm...
The challenge of facial biometrics is the decision rule of how to determine whether the claimant is the genuine user. It is an important issue that the decision rule affects the accuracy of performance. Therefore, the study proposes a breadth-first-based decision algorithm for facial biometrics. The proposed algorithm searches different graph paths to obtain a verified decision to accept or reject...
Interactive image search or relevance feedback is the process which helps a user refining his query and finding difficult target categories. This consists in partially labeling a very small fraction of an image database and iteratively refining a decision rule using both the labeled and unlabeled data. Training of this decision rule is referred to as transductive learning. Our work is an original...
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