Feature selection and machine learning: Deep diving into various methodologies for eliminating irrelevant features
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As artificial intelligence and machine learning address the larger challenges of solving the most herculean tasks, the importance of data analytics gains prominence. For instance, data analytics in the corporate sector involves dealing with a large number of records that involve different types of features. To extract the most relevant features from an ocean of digital information is what we talk about in this article.
The two most important aspects of feature learning that we focus on here are feature representation and feature selection. We aim to select the most prominent features in usage for data representation. At the same time, we aim to select the most relevant elements based on these features to drive the learning model. It needs to be noted at this point that we use both labeled and unlabeled data in the learning process.
The challenge with irrelevant features
To speak broadly, we can bifurcate the process of concept modeling into feature selection and feature combination. Before the selection of relevant features, it is necessary to eliminate the irrelevant ones. One of the most prominent approaches to eliminate the irrelevant features is to use induction algorithms. These algorithms help in downscaling many such irrelevant features. This allows the sample complexity to increase gradually with the increase in the number of features present. This also boosts the performance of the model. For instance, in a text classification problem, we may take a large number of features so that all the attributes are accommodated in the sample space. For the elimination of irrelevant features, the nearest neighbor method may be used. This method works by tracking the nearest stored attribute to form relevant subsets and allows us to eliminate those with insufficient information.
Heuristic search methodology
This is one of the most prominent methods which is taught in many ai and machine learning courses to eliminate a large number of irrelevant attributes. In this method, each state in the sample space specifies a category of relevant features. By this method, we can classify different feature selection tasks with the help of four elements that determine the nature of the heuristic analysis.
Embedded methodology for feature selection
This particular type of methodology relies on the usage of basic induction algorithms and the greedy set-cover algorithm to not only add or remove but also modify features in an extract. We also make use of partial ordering in this method to organize various elements in the sample space for effective search.
Other types of methods which can be employed for feature selection include the filter approach and wrapper approach. We can also make use of feature weighting methods to extract relevant features.
The process of feature selection is one of the most important techniques when it comes to the practical usage of deep learning. Although in its nascent stage, this process can attain sufficient maturity in the coming times with the expansion of the contours of machine learning.