Feature Extraction in Data Science

Here, we are going to learn about the feature extraction from a data set. the article gives a brief about the significances of feature extraction and its different application areas.
Submitted by Palkesh Jain, on March 09, 2021

Feature extraction is a dimensionality reduction mechanism by which an initial collection of raw data is reduced for processing to more manageable classes. A great number of variables that require, a lot of computational resources are used to process the massive data sets. Feature Extraction is the name for methods that pick and/or combine variables into features, essentially reducing the amount of data to be processed, while still representing the original data set accurately. Feature extraction is a method of attribute reduction. Unlike the collection of features, which ranks the current attributes according to their predictive importance, the attributes are transformed by feature extraction. Linear combinations of the original attributes are the transformed attributes, or characteristics.

The method of extracting the features results in a much smaller and richer set of attributes. Models based on extracted features can be of higher quality, since fewer, more meaningful attributes represent the data.

Feature extraction projects a data set to a smaller number of dimensions with greater dimensionality. As such, it is useful for data visualization, because when it is reduced to two or three dimensions, a complicated data set can be effectively visualized. Latent semantic analysis, data compression, data decomposition and projection, and pattern recognition are some feature extraction applications. To increase the speed and efficiency of supervised learning, feature extraction may also be used.

Example: Spam-detection program is one example of feature extraction that all of us can relate to. If we had a large set of emails and the keywords contained in these emails, then similarities between the different keywords could be identified through a feature extraction process.

Why Feature Extraction is Useful?

Extraction of features can help reduce the number of attributes that define the data. This can accelerate data mining using supervised learning methods. When we have a broad data set, the technique of extracting the characteristics is useful and you need to minimize the number of resources without missing any significant or relevant information. Extraction of functionality helps decrease the amount of redundant data from the data collection. In the end, data reduction helps to build the model with less effort from the computer and also increases the speed of learning and generalization steps in the process of machine learning.

Applications of Feature Extraction

  • Auto-Encoders: Effective data coding that is unsupervised is the main objective of auto-encoders. This approach is part of unsupervised learning. In order to define the key features of the data to code by learning from the coding of the original data set to derive new ones, the feature extraction process is therefore applicable here.
  • Bag of words: A natural language processing technique that extracts and classifies the words (features) used in a sentence, text, website, etc. by frequency of use, It is also possible to apply this technique to image processing.
  • Image Processing: One of the best and most interesting fields is image processing. So, we can use many techniques that can involve feature extraction and algorithms to detect features such as shapes, edges, or motion to process them in a digital image or video.


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