Many state-of-the-art face recognition algorithms use image descriptors based on features known as Local Binary Patterns (LBPs). While many variations of LBP exist, so far none of them can automatically adapt to the training data. We introduce and analyze a novel generalization of LBP that learns the most discriminative LBP-like features for each facial region in a supervised manner. Since the ...

Mainly understandable decision trees have been intended for perfect symbolic data. Conventional crisp decision trees (DT) are extensively used for classification purpose. However, there are still many issues particularly when we used the numerical (continuous valued) attributes. Structured continuouslabel classification is one type of classification in which the label is continuous in the data....

Bayesian averaging over Decision Trees (DTs) allows the class posterior probabilities to be estimated, while the DT models are understandable for domain experts. The use of Markov Chain Monte Carlo (MCMC) technique of stochastic approximation makes the Bayesian DT averaging feasible. In this paper we describe a new Bayesian MCMC technique exploiting a sweeping strategy allowing the posterior di...

This study aims to 1) develop Machine Learning Ecosystem models enhance grade point averages, and 2) predict averages by modeling techniques, namely, Decision Trees (DT), Naïve Bayes (NB), a Neural Network (NN). Findings from an efficiency comparison of the three in predicting showed that DT achieved highest accuracy 100.00%. In contrast, NN second-highest 85.83%, NB lowest 81.67%. For F-Measur...

This paper highlights the study of two classification methods, Rough Sets Theory (RST) and Decision Trees (DT), for the prediction of Learning Disabilities (LD) in school-age children, with an emphasis on applications of data mining. Learning disability prediction is a very complicated task. By using these two classification methods we can easily and accurately predict LD in any child. Also, we...

This paper highlights the two machine learning approaches, viz. Rough Sets and Decision Trees (DT), for the prediction of Learning Disabilities (LD) in school-age children, with an emphasis on applications of data mining. Learning disability prediction is a very complicated task. By using these two approaches, we can easily and accurately predict LD in any child and also we can determine the be...

In this paper we propose a synergistic melting of neural networks and decision trees (DT) we call neural decision trees (NDT). NDT is an architecture a la decision tree where each splitting node is an independent multilayer perceptron allowing oblique decision functions or arbritrary nonlinear decision function if more than one layer is used. This way, each MLP can be seen as a node of the tree...

in today’s quality- based competitive world, known as knowledge age, customer attraction is of ultimate importance. in respect to the slogan “customer is always right”, customer relation management is the core of an organizational strategy playing an important role in four aspects of customer identification, customer attraction, customer retaining, and customer satisfaction. commercial organiza...

As the most important responsibility of purchasing management, the problem of vendor evaluation and selection has always received a great deal of attention from practitioners and researchers. This management decision is a challenge due to the complexity and various criteria involved. This paper presents a hybrid model using data envelopment analysis (DEA), decision trees (DT) and neural network...

Classical crisp decision trees (DT) are widely applied to classiication tasks. Nevertheless, there are still a lot of problems especially when dealing with numerical (continuous-valued) attributes. Some of those problems can be solved using fuzzy decision trees (FDT). This paper proposes a method for handling continuous-valued attributes with automatically generated (as opposed to user deened) ...