Abstract
Nowadays, the high-dimensionality of data causes a variety of problems in machine learning. It is necessary to reduce the feature number by selecting only the most relevant of them. Different approaches called Feature Selection are used for this task. In this paper, we propose a Feature Selection method that uses Swarm Intelligence techniques. Swarm Intelligence algorithms perform optimization by searching for optimal points in the search space. We show the usability of these techniques for solving Feature Selection and compare the performance of five major swarm algorithms: Particle Swarm Optimization, Artificial Bee Colony, Invasive Weed Optimization, Bat Algorithm, and Grey Wolf Optimizer. The accuracy of a decision tree classifier was used to evaluate the algorithms. It turned out that the dimension of the data can be reduced about two times without a loss in accuracy. Moreover, the accuracy increased when abandoning redundant features. Based on our experiments GWO turned out to be the best. It has the highest ranking on different datasets, and its average iteration number to find the best solution is 30.8. ABC obtained the lowest ranking on high-dimensional datasets.
Highlights
We are living in an age of Big Data
Using more iterations would result in a long computational time
We showed the usability of Swarm Intelligence (SI) methods for Feature Selection (FS), implemented five SI
Summary
We are living in an age of Big Data. As the amount of data we collect is enormously growing all the time, it becomes more important to extract the relevant information. When using traditional model-based machine learning methods, high-dimensional data increase the search space and computational time. It can make noise, which affects the construction of the model, resulting in effectiveness loss. To solve problems arising from the high dimensionality of data, researchers mainly use two approaches: feature extraction and feature selection [1]. Feature extraction creates a low-dimensional feature space by combining some features. Feature selection removes redundant attributes and creates a small subset of features that are relevant for the model
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