Linear Sampling Method (LSM) is a simple and effective method for the shape reconstruction of unknown objects. It is also a fast and robust method to find the location of an object. This method is based on far field operator which relates the far field radiation to its More
Linear Sampling Method (LSM) is a simple and effective method for the shape reconstruction of unknown objects. It is also a fast and robust method to find the location of an object. This method is based on far field operator which relates the far field radiation to its associated line source in the object. There has been an extensive research on different aspects of the method. But from the experimental point of view there has been little research especially on the effect of polarization on the imaging quality of the method. In this paper, we study the effect of polarization on the quality of shape reconstruction of two dimensional targets. Some examples are illustrated to compare the effect of transverse electric (TE) and transverse magnetic (TM) polarizations, on the reconstruction quality of penetrable and non-penetrable objects.
Manuscript profile
Credit scoring is an important topic, and banks collect different data from their loan applicant to make an appropriate and correct decision. Rule bases are of more attention in credit decision making because of their ability to explicitly distinguish between good and b More
Credit scoring is an important topic, and banks collect different data from their loan applicant to make an appropriate and correct decision. Rule bases are of more attention in credit decision making because of their ability to explicitly distinguish between good and bad applicants. The credit scoring datasets are usually imbalanced. This is mainly because the number of good applicants in a portfolio of loan is usually much higher than the number of loans that default. This paper use previous applied rule bases in credit scoring, including RIPPER, OneR, Decision table, PART and C4.5 to study the reliability and results of sampling on its own dataset.
A real database of one of an Iranian export development bank is used and, imbalanced data issues are investigated by randomly Oversampling the minority class of defaulters, and three times under sampling of majority of non-defaulters class. The performance criterion chosen to measure the reliability of rule extractors is the area under the receiver operating characteristic curve (AUC), accuracy and number of rules. Friedman’s statistic is used to test for significance differences between techniques and datasets. The results from study show that PART is better and good and bad samples of data affect its results less.
Manuscript profile