Accurate Feature Elimination approach in Ensemble Learning on NSL-KDD dataset
Project Code :TCMAPY490
Objective
The aim of this project is to gain insights by studying and comparing different concept learning algorithms against ensemble learning techniques.
Abstract
The term
"learning" has numerous definitions. One of the most basic
definitions is: the activity or process of acquiring information or skill
through study, practice, instruction, or experience. There are different
classifications of learning strategies, just as there are various meanings for
learning. A couple examples include Ensemble Learning and Concept Learning. In
terms of machine learning, "concept learning" can be defined as: The problem
of searching through a predefined space of potential hypotheses for the
hypothesis that best fits the training examples. Much of human learning
involves acquiring general concepts from past experiences. For example, humans
identify different vehicles among all the vehicles based on specific sets of
features defined over a large set of features. This special set of features
differentiates the subset of cars in a set of vehicles. This set of features
that differentiate cars can be called a concept. Similarly, machines can learn
from concepts to identify whether an object belongs to a specific category by
processing past/training data to find a hypothesis that best fits the training
examples
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
HARDWARE SPECIFICATIONS:
RAM: 8GB (min)
Processor: I3/Intel Processor.
SOFTWARE SPECIFICATIONS:
Operating System: Windows 7+
IDE: Jupyter Notebook.
Libraries Used: Pandas, numpy, SKlearn, Pgmpy
Learning Outcomes
- What is Machine Learning?
- About Concept Learning.
- About Ensemble learning.
- Knowledge on Jupyter Notebook Editor.
- About Pandas.
- About Numpy.
Related Projects
Related Topics
Paper Publishing
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