A Generalized Wine Quality Prediction Framework by Evolutionary Algorithms

Wine is an exciting and complex product with distinctive qualities that makes it different from other manufactured products. Therefore, the testing approach to determine the quality of wine is complex and diverse. Several elements influence wine quality, but the views of experts can cause the most considerable influence on how people view the quality of wine. The views of experts on quality is very subjective, and may not match the taste of consumer. In addition, the experts may not always be available for the wine testing. To overcome this issue, many approaches based on machine learning techniques that get the attention of the wine industry have been proposed to solve it. However, they focused only on using a particular classifier with a specific set of wine dataset. In this paper, we thus firstly propose the generalized wine quality prediction framework to provide a mechanism for finding a useful hybrid model for wine quality prediction. Secondly, based on the framework, the generalized wine quality prediction algorithm using the genetic algorithms is proposed. It first encodes the classifiers as well as their hyperparameters into a chromosome. The fitness of a chromosome is then evaluated by the average accuracy of the employed classifiers. The genetic operations are performed to generate new offspring. The evolution process is continuing until reaching the stop criteria. As a result, the proposed approach can automatically find an appropriate hybrid set of classifiers and their hyperparameters for optimizing the prediction result and independent on the dataset. At last, experiments on the wine datasets were made to show the merits and effectiveness of the proposed approach

Replica Control Protocols That Guarantee High Availability and Low Access Cost

139 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1993.Making data instantly available is very important for nowadays industrial applications. The goal of this research is to provide database management schemes for distributed systems so that data can be efficiently accessed even if there are a lot of failures and busy sites in the distributed system.To achieve this goal, data are replicated at different sites of the system. Two schemes are presented to manage the replicated data, the triangular lattice scheme (18) and the new hierarchical quorum consensus scheme (17). It is proved that both schemes require access to the minimum number of data copies to maintain data consistency. This ensures that data can be efficiently accessed. In addition, both schemes offer asymptotically high availability. Thus data are available even if there are a lot of failures and busy sites in the system. The triangular lattice scheme has the advantage that the data access cost increases gradually as the number of failures and busy sites increases. The new hierarchical quorum consensus scheme has the advantage that the system designer can control the data availability and efficiency by adjusting parameters.Software has been implemented to simulate a distributed database system and to compare the performance of our schemes with that of existing schemes. Experiments show that our schemes outperform most existing schemes in both fault tolerance and efficiency.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD