Data Preprocessing for Supervised Leaning

By S. Kotsiantis, Dimitris N. Kanellopoulos, P. Pintelas. 2007

1,041 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/346675f9236b7f3d4873d1403a7aca0b0d78e589

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

A Study on WEKA Tool for Data Preprocessing, Classification and Clustering (2013, methodology): “[13] As seen in the Fig1 it database server module shows the steps of data pre-processing technologies or method used in data preprocessing includes data cleaning fig2 to eliminate the incorrect values and to check the inconsistency of the data.”
Novelty Detection and Learning from Extremely Weak Supervision (2019, methodology): “The pipeline of learning from data includes the following steps: 1)Pre-precessing, which includes different substeps like normalization/standardization, dealing with missing data, and feature selection [14].”
Decision tree supported substructure prediction of metabolites from GC-MS profiles (2010, methodology): “(2) For an improved feature construction with better discriminative potential (Kotsiantis et al. 2006), we implemented the full set of logarithmic pair-wise intensity ratios, thus, adding 137,550 spectral features to the DT training input space (‘‘ ratio lg ’’).”
Data preprocessing for anomaly based network intrusion detection: A review (2011, methodology): “Standard preprocessing steps include dataset creation, data cleaning, integration, feature construction to derive new higher-level features, feature selection to choose the optimal subset of relevant features, reduction, and discretisation (Kotsiantis et al., 2006).”
Comparison of Data Preprocessing Approaches for Applying Deep Learning to Human Activity Recognition in the Context of Industry 4.0 (2018, methodology): “Data preprocessing plays an important role in machine learning and deep learning algorithms, and proper preprocessing of data is compulsory for achieving better HAR performance [26,27].”
A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing (2019, methodology): “The preprocessing step can often have a signiﬁcant impact on the gen-400 eralized performance of a SML algorithm and may include sub-steps, such as data cleaning, normalization, transformation, feature extraction and se-402 lection, etc. [111].”