Handling missing values in trait data
نویسندگان
چکیده
منابع مشابه
Handling Missing Values in Data Mining
Missing Values and its problems are very common in the data cleaning process. Several methods have been proposed so as to process missing data in datasets and avoid problems caused by it. This paper discusses various problems caused by missing values and different ways in which one can deal with them. Missing data is a familiar and unavoidable problem in large datasets and is widely discussed i...
متن کاملHandling Missing Attribute Values
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, i.e., knowledge...
متن کاملHandling Missing Attribute Values in Preterm Birth Data Sets
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 spe...
متن کاملHandling of Missing Values in Lexical Acquisition
We propose a strategy to reduce the impact of the sparse data problem in the tasks of lexical information acquisition based on the observation of linguistic cues. It justifies that the uncertainty created by missing values, i.e. non-observed cues, can be handled by estimating its likelihood of being observable. Because of the Zipfian distribution of words, instead of estimating the likelihood f...
متن کاملHandling Missing Values when Applying Classification Models
Much work has studied the effect of different treatments of missing values on model induction, but little work has analyzed treatments for the common case of missing values at prediction time. This paper first compares several different methods—predictive value imputation, the distributionbased imputation used by C4.5, and using reduced models—for applying classification trees to instances with...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Global Ecology and Biogeography
سال: 2020
ISSN: 1466-822X,1466-8238
DOI: 10.1111/geb.13185