In this article, we investigate the features which enhanced discriminate the survival in the micro and small business (MSE) using the approach of data mining with feature selection. According to the complexity of the data set, we proposed a comparison of three data imputation methods such as mean imputation (MI), k-nearest neighbor (KNN) and expectation maximization (EM) using mutually the selection of variables technique, whereby t-test, then through the data mining process using logistic regression classification methods, naive Bayes algorithm, linear discriminant analysis and support vector machine hence comparing their respective performances. The experimental results will be spread in developing a model to predict the MSE survival, providing a better understanding in the topic once it is a significant part of the Brazilian' GPA and macroeconomy.
We introduce the notion of restricted Bayes optimal classifiers. These classifiers attempt to combine the flexibility of the generative approach to classification with the high accuracy associated with discriminative learning. They first create a model of the joint distribution over class labels and features. Instead of choosing the decision boundary induced directly from the model, they restrict the allowable types of decision boundaries and learn the one that minimizes the probability of misclassification relative to the estimated joint distribution. In this paper, we investigate two particular instantiations of this approach. The first uses a nonparametric density estimator -- Parzen Windows with Gaussian kernels -- and hyperplane decision boundaries. We show that the resulting classifier is asymptotically equivalent to a maximal margin hyperplane classifier, a highly successful discriminative classifier. We therefore provide an alternative justification for maximal margin hyperplane classifiers. The second instantiation uses a mixture of Gaussians as the estimated density; in experiments on real-world data, we show that this approach allows data with missing values to be handled in a principled manner, leading to improved performance over regular discriminative approaches.
This paper focuses on a comparative evaluation of the most common and modern methods for text classification, including the recent deep learning strategies and ensemble methods. The study is motivated by a challenging real data problem, characterized by high-dimensional and extremely sparse data, deriving from incoming calls to the customer care of an Italian phone company. We will show that deep learning outperforms many classical (shallow) strategies but the combination of shallow and deep learning methods in a unique ensemble classifier may improve the robustness and the accuracy of "single" classification methods.
The aim of this paper is to compare Bayesian network classifiers to the k-NN classifier based on a subset of features. This subset is established by means of sequential feature selection methods. Experimental results show that Bayesian network classifiers more often achieve a better classification rate on different data sets than selective k-NN classifiers. The k-NN classifier performs well in the case where the number of samples for learning the parameters of the Bayesian network is small. Bayesian network classifiers outperform selective k-NN methods in terms of memory requirements and computational demands. This paper demonstrates the strength of Bayesian networks for classification.