The occurrences of noisy data in data set can significantly impact prediction of any
meaningful information. Many empirical studies have shown that noise in data set …

Characteristics extracted from the training datasets of classification problems have proven to
be effective predictors in a number of meta-analyses. Among them, measures of …

The k‐nearest neighbors algorithm is characterized as a simple yet effective data mining
technique. The main drawback of this technique appears when massive amounts of data …

Data preprocessing is an important step for designing classification model. Normalization is
one of the preprocessing techniques used to handle the out-of-bounds attributes. This work …

Supervised classification systems used for land cover map** require accurate reference
databases. These reference data come generally from different sources such as field …

Machine learning techniques will contribution towards making Internet of Things (IoT)
symmetric applications among the most significant sources of new data in the future. In this …

The imbalanced data classification is one of the most crucial tasks facing modern data
analysis. Especially when combined with other difficulty factors, such as the presence of …

Rate of Penetration (ROP) can be considered as a crucial factor in optimization and cost
minimization of drilling operations. In order to predict ROP with satisfactory precision, some …

In supervised learning model development, domain experts are often used to provide the
class labels (annotations). Annotation inconsistencies commonly occur when even highly …

Network Traffic Classification (NTC) has become an important feature in various network
management operations, eg, Quality of Service (QoS) provisioning and security services …