Rough, tough and dependable

Some of your customers could care less what kind of zipper they find in their clothes. All they do is give it the roughest workout.

Rough sketch of butter concepts

"Roughs" of butter concepts, showing a woman preparing peas with butter

Rough draft of the script for "The Swimmer"

A draft with handwritten notes of "The Swimmer" script for use in the 1984 nutrition campaign.

Improving the Scalability of Reduct Determination in Rough Sets

Rough Set Data Analysis (RSDA) is a non-invasive data analysis approach that solely relies on the data to find patterns and decision rules. Despite its noninvasive approach and ability to generate human readable rules, classical RSDA has not been successfully used in commercial data mining and rule generating engines. The reason is its scalability. Classical RSDA slows down a great deal with the larger data sets and takes much longer times to generate the rules. This research is aimed to address the issue of scalability in rough sets by improving the performance of the attribute reduction step of the classical RSDA - which is the root cause of its slow performance. We propose to move the entire attribute reduction process into the database. We defined a new schema to store the initial data set. We then defined SOL queries on this new schema to find the attribute reducts correctly and faster than the traditional RSDA approach. We tested our technique on two typical data sets and compared our results with the traditional RSDA approach for attribute reduction. In the end we also highlighted some of the issues with our proposed approach which could lead to future research.

GA approach for finding Rough Set decision rules based on bireducts

Feature selection plays an important role in knowledge discovery and data mining nowadays. In traditional rough set theory, feature selection using reduct - the minimal discerning set of attributes - is an important area. Nevertheless, the original definition of a reduct is restrictive, so in one of the previous research it was proposed to take into account not only the horizontal reduction of information by feature selection, but also a vertical reduction considering suitable subsets of the original set of objects. Following the work mentioned above, a new approach to generate bireducts using a multi--objective genetic algorithm was proposed. Although the genetic algorithms were used to calculate reduct in some previous works, we did not find any work where genetic algorithms were adopted to calculate bireducts. Compared to the works done before in this area, the proposed method has less randomness in generating bireducts. The genetic algorithm system estimated a quality of each bireduct by values of two objective functions as evolution progresses, so consequently a set of bireducts with optimized values of these objectives was obtained. Different fitness evaluation methods and genetic operators, such as crossover and mutation, were applied and the prediction accuracies were compared. Five datasets were used to test the proposed method and two datasets were used to perform a comparison study. Statistical analysis using the one-way ANOVA test was performed to determine the significant difference between the results. The experiment showed that the proposed method was able to reduce the number of bireducts necessary in order to receive a good prediction accuracy. Also, the influence of different genetic operators and fitness evaluation strategies on the prediction accuracy was analyzed. It was shown that the prediction accuracies of the proposed method are comparable with the best results in machine learning literature, and some of them outperformed it.

Rough copy of the balance sheet to May 31, 1882

Rough copy of the balance sheet (3 pages, handwritten) to May 31, 1882.

Rough draft of a letter to John I. Mackenzie [from S.D. Woodruff]

Rough draft of a letter to John I. Mackenzie [from S.D. Woodruff]. The letter is illegible (1 doublesided page, handwritten), Dec. 6, 1881.

Letter (rough copy) written to Colonel Hope, commander of the Queen’s Volunteers from J.P. Bradley

Letter (rough copy) written to Colonel Hope, commander of the Queen’s Volunteers from J.P. Bradley offering his services (3 pages, handwritten). Bradley asks why he was not appointed to the new corps, Nov. 8, 1838.