Classification Strategies Using Certain and Possible Rules

Grzymała-Busse, Jerzy W.; Zou, Xihong

doi:10.1007/3-540-69115-4_6

Cited by 26 publications

(13 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the experiments, four combinations of using rule sets were applied: using only certain rules, using only possible rules, using certain rules first then possible rules if necessary, and using both certain and possible rules. The option complete matching, then partial matching if necessary is better than the option using both complete matching and partial matching [5], so only that first option was used.…”

Section: Methodsmentioning

confidence: 99%

Handling Missing Attribute Values in Preterm Birth Data Sets

Grzymała-Busse

Goodwin

Grzymala-Busse³

et al. 2005

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

Abstract. 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.

show abstract

Section: Methodsmentioning

confidence: 99%

Handling Missing Attribute Values in Preterm Birth Data Sets

Grzymała-Busse

Goodwin

Grzymala-Busse³

et al. 2005

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…On the other hand, rules induced from the upper approximation of the class describe only possible (or plausible) cases, so they are called possible. 11 Other data mining systems based on rough set theory are described in Ref. 17.…”

Section: Rule Induction and Classification Of Unseen Casesmentioning

confidence: 99%

A comparison of three closest fit approaches to missing attribute values in preterm birth data

Grzymała-Busse¹,

Grzymala-Busse²,

Goodwin³

2002

Int. J. Intell. Syst.

Self Cite

View full text Add to dashboard Cite

One of the main problems of data mining is imperfection of input data. Such data may be uncertain, vague, and incomplete. In our data set, describing preterm birth, many attribute values were missing, that is, the input data set was incomplete. The main approach to solving the missing attribute value problem was based on a closest fit: a missing attribute value in a case was replaced by the existing attribute value in the best candidate, a case that fits as closely as possible (resembles the most) the case with the missing attribute value. We experimented with three methods based on the idea of the closest fit: looking for the best candidate among the set of all cases, among the cases that belong to the same concept (cases within the same class as the case with missing attribute values), and a special method, where the set of all attributes was restricted to a single attribute with the missing attribute value. In the last method, the missing attribute value was replaced by the most common value within the concept for symbolic attributes, and by the average value of all attribute values of the same concept for numerical attributes.

show abstract

“…doi:10.1016/j.ins.2007. 10.006 an incomplete information system we mean a system that the values of some of the attributes are not known, i.e., missing or partially known, an incomplete information system is also called a nondeterministic information system [38].…”

Section: Introductionmentioning

confidence: 99%

Attribute reduction based on evidence theory in incomplete decision systems

2008

Information Sciences

179

View full text Add to dashboard Cite

Classification Strategies Using Certain and Possible Rules

Cited by 26 publications

References 6 publications

Handling Missing Attribute Values in Preterm Birth Data Sets

Handling Missing Attribute Values in Preterm Birth Data Sets

A comparison of three closest fit approaches to missing attribute values in preterm birth data

Attribute reduction based on evidence theory in incomplete decision systems

Contact Info

Product

Resources

About