From Machine Learning to Deep Learning | Part4 First Application : Feature Selection in java
In these tutorial series we will learn about machine learning algorithms and implementation of them in java. After learning completely about machine learning we will learn how we can improve the result of some problems with deep learning.
Waikato Environment for Knowledge Analysis (WEKA)is a machine learning library that was developed at the University of Waikato, New Zealand, and is probably the most well-known Java library. It is a general purpose library that is
able to solve a wide variety of machine learning tasks, such as classification, regression, and clustering. It features a rich graphical user interface, command-line interface, and Java API.
The Java Machine Learning Library (Java-ML) is a collection of machine learning algorithms with a common interface for algorithms of the same type. It only features the Java API, and so it is primarily aimed at software engineers and programmers. Java-ML contains algorithms for data preprocessing, feature selection, classification, and clustering.
Information gain is used for determining the best features/attributes that render maximum information about a class. It follows the concept of entropy while aiming at decreasing the level of entropy, beginning from the root node to the leaf nodes.
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