Filters for Electromagnetic Compatibility Applications
preprocessing
1. 1. What is preprocessing?
. It is the state of the program that occurs BEFORE any code is compiled
.At this point, no values are initialized and nothing is evaluated
.Code swapping and replacing can occur here, but no computations
3. Data Preprocessing
• Data preprocessing is an important step in the data mining process. The
phrase "garbage in, garbage out" is particularly applicable to data mining and machine
learning projects.
• Data-gathering methods are often loosely controlled, resulting
in out-of-range values (e.g., Income: −100), impossible data
combinations (e.g., Sex: Male, Pregnant: Yes), missing values, etc.
Analyzing data that has not been carefully screened for such
problems can produce misleading results. Thus, the representation
and quality of data is first and foremost before running an
analysis.[1] Often, data preprocessing is the most important phase of
a machine learning project
4. If there is much irrelevant and redundant information present or
noisy and unreliable data, then knowledge discovery during the training
phase is more difficult.
Data preparation and filtering steps can take considerable amount of
processing time.
Data preprocessing includes cleaning, Instance
selection, normalization, transformation, feature
extraction and selection, etc. The product of data preprocessing is the
final training set.
5. • Here are some brief introductions for the methods in the data preprocessing step.
Data cleaning is the process of detecting, correcting or removing the inaccurate
records from data;
• [3] Data normalization is the process used to standardize the range of
independent variables or features of data into [0, 1] or [-1, +1];
• [4] Data transformation is the process of converting data from a format to
the new format people expect
• [5] Feature extraction is the process of transforming the input data into a
set of features which can very well represent the input data;
• [6] Data reduction is the transformation of numerical data into a corrected,
ordered, and simplified form, minimizing the amount of data or reducing
the dimensionality of data.
