An overview of decision table literature 1982-1995.

This report gives an overview of the literature on decision tables over the past 15 years. As much as possible, for each reference, an author supplied abstract, a number of keywords and a classification are provided. In some cases own comments are added. The purpose of these comments is to show where, how and why decision tables are used. The literature is classified according to application area, theoretical versus practical character, year of publication, country or origin (not necessarily country of publication) and the language of the document. After a description of the scope of the interview, classification results and the classification by topic are presented. The main body of the paper is the ordered list of publications with abstract, classification and comments.

Preprocessing and Feature Selection on Group Structure Analysis using Entropy and Thresholding

Many real data increase dynamically in size. We have been observing in many fields that data grow with time in size. This has led to the development of several new analytic techniques. This phenomenon occurs in several fields including economics, population studies, and medical research. As an effective and efficient mechanism to deal with such data, incremental technique has been proposed in the literature and attracted much attention, which stimulates the result in this paper. When a group of objects are added to a decision table, we first introduce incremental mechanisms for three representative information entropies and then develop a group incremental rough feature selection algorithm based on information entropy.When multiple objects are added to a decision table, the algorithm aims to find the new feature subset in a much shorter time. Experiments have been carried out on eight UCI data sets and the experimental results show that the algorithm is effective and efficient

Прецедентний метод визначення розпливчатих меж безпечних областей у разі спільного руху засобів пересування

The case-based assessment method of determining the vague boundaries of safety domains inuncertainty situations using the rough set approach is considered. The construction of spatialconfigurations are described, method of determining the spatio-temporal similarity function is proposed.The proposed method is not sensitive to imprecise and incomplete observations due to using the roughsets to determine dynamic safety domainsРассмотрен прецедентный метод оценивания для определения расплывчатых границ областей безопасности вситуациях неопределенности с использованием подхода на основе приближенных множеств. Описано построение пространственных конфигураций, предложен способ определения пространственно-временнойфункции сходства. Предложенный метод является не чувствительным к неточным и неполным наблюдениям вследствие использования приближенных множеств для определения динамических доменов безопасностиРозглянуто прецедентний метод оцінювання для визначення розпливчатих меж областей безпеки в ситуаціях невизначеності з використанням підходу на основі наближених множин. Описано побудову просторових конфігурацій, запропоновано спосіб визначення просторово-часової функції подібності. Запропонований метод є не чутливим до неточних і неповних спостережень внаслідок використання наближених множин для визначення динамічних доменів безпек

Active Sample Selection Based Incremental Algorithm for Attribute Reduction with Rough Sets

Attribute reduction with rough sets is an effective technique for obtaining a compact and informative attribute set from a given dataset. However, traditional algorithms have no explicit provision for handling dynamic datasets where data present themselves in successive samples. Incremental algorithms for attribute reduction with rough sets have been recently introduced to handle dynamic datasets with large samples, though they have high complexity in time and space. To address the time/space complexity issue of the algorithms, this paper presents a novel incremental algorithm for attribute reduction with rough sets based on the adoption of an active sample selection process and an insight into the attribute reduction process. This algorithm first decides whether each incoming sample is useful with respect to the current dataset by the active sample selection process. A useless sample is discarded while a useful sample is selected to update a reduct. At the arrival of a useful sample, the attribute reduction process is then employed to guide how to add and/or delete attributes in the current reduct. The two processes thus constitute the theoretical framework of our algorithm. The proposed algorithm is finally experimentally shown to be efficient in time and space