http://bcpw.bg.pw.edu.pl/Content/1968/Rudiments.pdf

Rudiments of rough sets

A systematic study of the class imbalance problem in convolutional neural networks

Rare objects are often of great interest and great value Until recently, however, rarity has not received much attention in the context of data mining Now, as increasingly complex real-world problems are addressed, rarity, and the related problem of imbalanced data, are taking center stage This article discusses the role that rare classes and rare cases play in data mining The problems that can result from these two forms of rarity are described in detail, as are methods for addressing these problems These descriptions utilize examples from existing research So that this article provides a good survey of the literature on rarity in data mining This article also demonstrates that rare classes and rare cases are very similar phenomena---both forms of rarity are shown to cause similar problems during data mining and benefit from the same remediation methods

/pdf/mining-with-rarity-a-unifying-framework-2o94yf2lq2.pdf

Mining with rarity: a unifying framework

Rough sets and Boolean reasoning

The present article provides a survey of the available literature on data mining using soft computing. A categorization has been provided based on the different soft computing tools and their hybridizations used, the data mining function implemented, and the preference criterion selected by the model. The utility of the different soft computing methodologies is highlighted. Generally fuzzy sets are suitable for handling the issues related to understandability of patterns, incomplete/noisy data, mixed media information and human interaction, and can provide approximate solutions faster. Neural networks are nonparametric, robust, and exhibit good learning and generalization capabilities in data-rich environments. Genetic algorithms provide efficient search algorithms to select a model, from mixed media data, based on some preference criterion/objective function. Rough sets are suitable for handling different types of uncertainty in data. Some challenges to data mining and the application of soft computing methodologies are indicated. An extensive bibliography is also included.

/pdf/data-mining-in-soft-computing-framework-a-survey-4fzqfcxb58.pdf

Data mining in soft computing framework: a survey

In this chapter methods of handling missing attribute values in Data Mining are described These methods are categorized into sequential and parallel In sequential methods, missing attribute values are replaced by known values first, as a preprocessing, then the knowledge is acquired for a data set with all known attribute values In parallel methods, there is no preprocessing, ie, knowledge is acquired directly from the original data sets In this chapter the main emphasis is put on rule induction Methods of handling attribute values for decision tree generation are only briefly summarized

Handling Missing Attribute Values

. This chapter describes experiments with a challenging data set describing preterm births. The data set, collected at the Duke University Medical Center, was large but at the same time many attribute values were missing. However, the main problem was that only 20.7% of the total number of cases represented the important preterm birth class. Thus, the data set was imbalanced. For comparison, we include results of experiments on another imbalanced data set, the well-known breast cancer data set.

/pdf/an-approach-to-imbalanced-data-sets-based-on-changing-rule-3wcvqsl1pq.pdf

An Approach to Imbalanced Data Sets Based on Changing Rule Strength

The objective of our research was to find the best approach to handle missing attribute values in data sets describing preterm birth provided by the Duke University. Five strategies were used for filling in missing attribute values, based on most common values and closest fit for symbolic attributes, averages for numerical attributes, and a special approach to induce only certain rules from specified information using the MLEM2 approach. The final conclusion is that the best strategy was to use the global most common method for symbolic attributes and the global average method for numerical attributes.

/pdf/handling-missing-attribute-values-in-preterm-birth-data-sets-3ddbpegwe2.pdf

Handling missing attribute values in preterm birth data sets

In this paper we present results of experiments conducted to compare three types of missing attribute values: lost values, "do not care" conditions and attribute-concept values. For our experiments we selected six well known data sets. For every data set we created 30 new data sets replacing specified values by three different types of missing attribute values, starting from 10%, ending with 100%, with increment of 10%. For all concepts of every data set concept lower and upper approximations were computed. Error rates were evaluated using ten-fold cross validation. Overall, interpreting missing attribute values as lost provides the best result for most incomplete data sets.

An experimental comparison of three rough set approaches to missing attribute values

Data mining is frequently applied to data sets with missing attribute values. A new approach to missing attribute values, called closest fit, is introduced in this paper. In this approach, for a given case (example) with a missing attribute value we search for another case that is as similar as possible to the given case. Cases can be considered as vectors of attribute values. The search is for the case that has as many as possible identical attribute values for symbolic attributes, or as the smallest possible value differences for numerical attributes. There are two possible ways to conduct a search: within the same class (concept) as the case with the missing attribute values, or for the entire set of all cases. For comparison, we also experimented with another approach to missing attribute values, where the missing values are replaced by the most common value of the attribute for symbolic attributes or by the average value for numerical attributes. All algorithms were implemented in the system OOMIS. Our experiments were performed on the preterm birth data sets provided by the Duke University Medical Center.

Witold J. Grzymala-Busse

Papers

Handling Missing Attribute Values

An Approach to Imbalanced Data Sets Based on Changing Rule Strength

Handling missing attribute values in preterm birth data sets

An experimental comparison of three rough set approaches to missing attribute values

Coping With Missing Attribute Values Based on Closest Fit in Preterm Birth Data: A Rough Set Approach