Econometric Modelling: Basics

This chapter addresses basic topics related to choice data analysis. It starts by describing the coding of attribute levels and choosing the functional form of the attributes in the utility function. Next, it focuses on econometric models with special attention devoted to the random parameter mixed logit model. In this context, the chapter compares different coefficient distributions to be used, addresses specifics of the cost attribute coefficient and it pays attention to potential correlations between random coefficients. Finally, topics related to the estimation procedure such as assuring its convergence or random draws are discussed.

Decision Support System to Determine Applicant Housing Credits With SAW Method on the House Complex of J. City Residence by Capital Property

J. City Residence provides subsidized housing loans facilities for people who earn below the average. The number of credit applicants with different criteria requires carefulness of the Credit Analyst in making decisions. This problem can be solved by building a Decision Support System (DSS) in determining the provision of subsidized mortgage loans using the Simple Additive Weighting (SAW) method. The criteria used are house condition (cost attribute), income (cost attribute), employment (benefit attribute), credit history (benefit attribute) and marital status (benefit attribute). The process is to normalize the credit applicant value matrix, then multiply the results of the normalization by the weight value. If the result of the calculation is above the credit line is not feasible, then the applicant is declared eligible to receive credit. Application can be used to help to determine the eligibility of consumers in obtaining subsidized housing loans with the SAW method in J. City Residence by Capital Property Housing.

FAST RANDOMIZED ALGORITHM FOR MINIMAL TEST COST ATTRIBUTE REDUCTION

Attribute reduction is a key data preprocessing technique, and has been widely studied in data mining, machine learning, and granular computing. Minimal test cost attribute reduction is one of important parts researched in cost-sensitive learning. The backtracking algorithm can obtain an optimal reduct, however on only small datasets due to the NP-hardness of the problem. Heuristic algorithms, such as the genetic one and the information gain based one, are employed to deal with this problem. In this paper, we propose the Fast Randomized Algorithm to obtain a satisfactory reduct more efficiently. The focus of the algorithm is a randomization mechanism that deals with attributes addition and deletion. There are two important parameters in the addition stage, namely the selecting probability of attributes and the number of selected attributes per batch. We obtain some appropriate parameter settings through experiments in a variety of datasets. Results show that the optimal settings of two parameters rarely change on different datasets. Our algorithm is more stable and significantly more efficient than existing heuristic ones.