Extended Rough Set Models And Their Applications In Quality Prediction And Evaluation For Tobacco Leaves

Rough set theory is an efficient machine learning tool which deals with imprecise, incomplete and inconsistent data. However, Pawlak's traditional rough set theory shows its limitations when facing some kinds of uncertainties in reality. In order to get better use in the practical process, some improvements and extentions of Pawlak's traditional rough set theory and related models are proposed. The rationality and and effectiveness of the improved algorithms and extended rough set models are evaluated through a large number of experiments and corresponding theoretical proofs. Further more, theoretical research results are applied to quality prediction and evaluation for flue-cured tobacco leaves, which prove that rough set theory has advantages over other methods when solving similar problems.The whole article is composed of four parts. The first two parts focus on rough set theory and related models, the latter two parts are about applications of quality prediction and evaluation for flue-cured tobacco leaves based on rough set theory.In aspect of theoretical research, three key problems in rough set theory are discussed, which include discretization problem, knowledge reduction problem and problem of rule extraction for further reasoning. As to discretization problem, three new heuristic criterions are put forward by considering the uniqueness of rough set theory, and a novel algorithm named"half-global discretization algorithm"is presented. As to knowledge reduction problem, in order to eliminate troubles aroused by inconsistent data during attribute reduction, a new conditional attribute reduction algorithm based on improved discernibility is presented, which takes conditional entropy into account. After that, an incremental conditional attribute reduction algorithm is developed. As to the problem of rule extraction for reasoning and decision, the uncertainty solving routines of rough set theory, which is well known as a kind of machine learning tool based on uncertainty reasoning, is elucidated. In addition, steps of rule extraction for reasoning and related modeling processes of rough set theory are summarized.In chapter3, on the basis of Pawlak's rough set theory and considering data uncertainty and knowledge uncertainty in reality, three extended rough set models--interval data rough set model, hybrid data rough set model and incomplete data rough set model based on general similarity are presented. Each extended rough set model deals with specific uncertainties. For each extended rough set model, interrelated modeling methods and reasoning algorithms are presented. These new models were proved by corresponding theoretical and then validated by simulation experiments. In addition, regarding the widely existing problem of threshold determination in extended rough set models, we analyzed all rough set theory related definitions and designed the optimization computing algorithms to choose the two threshold values. In aspect of application research, we firstly focus on two major aspects in quality prediction for flue-cured tobacco leaves—appearance quality and inherent quality. In order to obtain more reasonable values of evaluation indexes for tobacco leaves'appearance quality and inherent quality, to score the evaluation indexes automatically and intellectualized, and to establish good foundation for the upcoming synthetic quality evaluation for flue-cured tobacco leaves, we predict values of some evaluation indexes which rely on manual work in usual, based on conventional chemical compounds of flue-cured tobacco leaves and proper rough set models. Furthermore, in virtue of the rules acquired by rough set theory, mapping relationships between each evaluation index and chemical compounds are brought forth perfectly. Through comparable experiments based on flue-cured tobacco leaves'historical data, it can fully prove the feasibility and advantage of using rough set theory and the proposed extended rough set models.As to quality evaluation problem for flue-cured tobacco leaves, we firstly studied conventional quality evaluation method which derives from flue-cured tobacco leaves grading. In this section, two-level reasoning model based on rough set theory is built up, which can reach refined evaluation results and also provide a feasible way to deal with large-scale database by using rough set model. Then, by summarizing current research results for flue-cured tobacco leaves'synthetic quality evaluation, a relatively comprehensive tobacco leaf quality evaluation index system is defined. Lastly, by combining the multi-attribute decision making method based on the objective and subjective synthetic approach to determine weight, a flue-cured tobacco leaves'synthetic quality evaluation and decision model based on rough set theory is proposed for the first time. Examples are given to expatiate the given evaluation model.