By Wei (ed.)
This lawsuits quantity comprises chosen papers offered on the 2014 AASRI foreign convention on utilized Engineering Sciences, held in Hollywood, los angeles, united states. Contributions hide the newest advancements and advances within the box of utilized Engineering Sciences.
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The set of routines deals insights and perform in employing rules for bettering the layout of latest laptop code. Readers are anticipated to be practising programmers or graduate and upper-level undergraduate scholars who've already been uncovered to the rules themselves.
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Regularized Negative Correlation Learning (RNCL)  improves neural network ensemble performance, adding a regularization term with the objective of a reduced over-fitting problem. In the RNCL algorithm, each network m has the following error function: b) Extract a sample xi randomly from minority class np ; c) Find out the k number of samples nearest the xi and randomly extract a sample xn from the k samples and generate a random number vran ∈ [0, 1]; d) Generate required synthetic instances xs by interpolating between the seeds and minority nearest neighbor examples xs = xi + vran · · · (xn − xi ); e) repeat b) to d) until generating ns = b · np ; (b is the rate of minority data, ns is the number of man-made data sets).
In this paper, we study pre-sampling-NNE (PSNNE) classifiers. First, a new training data set can be generated by an under-sampling and SMOTE oversampling hybrid method from the original examples. Then, every based classifier was trained on a new data set, while the regularized negative correlation penalty terms were included in the cost function of NNE, in order to overcome over-fitting and promote the ensembles’ generalization ability. Our contributions are to propose hybrid pre-sampling and neural network ensembles with an RNCL algorithm for imbalanced data classification.
Table 2. The basic statistical properties of 4 data sets extracted from the benchmark data sets. Figure 2. Illustrates the result of knowledge transfer based on a prototype model. Table 1. Recommended items for each user based on the scoring matrix in Figure 2. 86% Target domain Target domain in various recommendation algorithms in the research community. We set Movielens 1M as the auxiliary e-commerce site and the other three are the target ones. The items of Movielens and Netfilx include media products, and those of Amazon involve diversified products.
Applied Engineering Sciences by Wei (ed.)