A. Bounceur

A New Method for Estimation of Missing Data Based on Sampling Methods for Data Mining

Today we collect large amounts of data and we receive more than we can handle, the accumulated data are often raw and far from being of good quality they contain Missing Values and noise.

Handling Missing Data Problems with Sampling Methods

Missing data cases are a problem in all types of statistical analyses and arise in almost all application domains. Several schemes have been studied in this paper to overcome the drawbacks produced by missing values in data mining tasks, one of the most well known is based on pre processing, formerly known as imputation. In this work, we propose a new multiple imputation approach based on sampling techniques to handle missing values problems, in order to improving the quality and efficiency of data mining process. The proposed method is favourably compared with some imputation techniques and outperforms the existing approaches using an experimental benchmark on a large scale, waveform dataset taken from machine learning repository and different rate of missing values (till 95%).

A new method for dimensionality reduction of multi-dimensional data using Copulas

A new technique for the Dimensionality Reduction of Multi-Dimensional Data is presented in this paper. This technique employs the theory of Copulas to estimate the multivariate joint probability distribution without constraints to specific types of marginal distributions of random variables that represent the dimensions of our Data. A Copulas-based model, provides a complete and scale-free description of dependence that is more suitable to be modeled using well-known multivariate parametric laws. The model can be readily used for comparing of dependence of random variables by estimating the parameters of the Copula and to better see the relationship between data. This dependence is thereafter used for detecting the Redundant Values and noise in order to clean the original data, reduce them (eliminate Redundant attributes) and obtain representative Samples of good quality. We compared the proposed approach with singular values decomposition (SVD) technique, one of the most efficient method of Data mining.

Analog performance prediction based on archimedean copulas generation algorithm

Testing analog circuits is a complex and very time consuming task. In contrary to digital circuits, testing analog circuits needs different configurations, each of them targets a certain set of output parameters which are the performances and the test measures. One of the solutions to simplify the test task and optimize test time is the reduction of the number of to-be-tested performances by eliminating redundant ones. However, the main problem with such a solution is the identification of redundant performances. Traditional methods based on calculation of the correlation between different performances or on the defect level are shown to be not sufficient. This paper presents a new method based on the Archimedean copula generation algorithm. It predicts the performance value from each output parameter value based on the dependence (copula) between the two values. Therefore, different performances can be represented by a single output parameter; as a result, less test configurations are required. To validate the proposed approach, a CMOS imager with two performances and one test measure is used. The simulation results show that the two performances can be replaced by a single test measure. Industrial results are also reported to prove the superiority of the proposed approach.1

Estimation of test metrics for multiple analogue parametric deviations

The estimation of test metrics such as defect level, test yield or yield loss is important in order to quantify the quality and cost of a test approach. In the analogue domain, previous works have considered the estimation of these metrics for the case of single faults, either catastrophic or parametric. The consideration of single parametric faults is sensible for a production test technique if the design is robust. However, in the case that production test limits are tight, test escapes resulting from multiple parametric deviations become important. In addition, aging mechanisms result in field failures that are often caused by multiple parametric deviations. In this paper, we present a statistical technique for estimating test metrics for the case of multiple analogue parametric deviations, requiring a Monte Carlo simulation of the circuit under test. This technique assumes Gaussian probability density functions (PDFs) for the parameter and performance deviations but the technique can be adapted to other types of PDFs. We will illustrate the technique for the case of testing a fully differential operational amplifier, proving the validity in the case of this circuit of the Gaussian PDF