Abstract
Feature selection has emerged as a craft, using which we boost the performance of our learning model. Feature or Attribute Selection is a data preprocessing technique, where only the most informative features are considered and given to the predictor. This reduces the computational overhead and improves the correctness of the classifier. Attribute Selection is commonly carried out by applying some filter or by using the performance of the learning model to gauge the quality of the attribute subset. Metric Ranked Feature Inclusion and Accuracy Ranked Feature Inclusion are the two novel hybrid feature selection methods we introduce in this paper. These algorithms follow a two-stage procedure, the first of which is feature ranking, followed by feature subset selection. They differ in the way they rank the features but follow the same subset selection technique. Multiple experiments have been conducted to assess our models. We compare our results with numerous works of the past and validate our models using 12 datasets. From the results, we infer that our algorithms perform better than many existent models.
Highlights
M ACHINE Learning algorithms are used to extract certain information from data, with the help of statistical models
The filter phase of Metric Ranked Feature Inclusion (MRFI) has a time complexity of O(n ∗ k ∗ d ∗ i) where n is the number of data samples, k is the number of clusters, d is the attribute count and i denotes the number of iterations that occur
STATISTICAL ANALYSIS To further support the comparative analysis shown in Tables 2 and 3, statistical tests were performed to gauge the performance of MRFI and Accuracy Ranked Feature Inclusion (ARFI) in comparison with Recursive Feature Elimination (RFE)
Summary
M ACHINE Learning algorithms are used to extract certain information from data, with the help of statistical models. FS algorithms using the filter technique, pick features based on some score or statistical measure that is allocated to each feature. The predictor is not considered while choosing the best subset of variables in the filter approach These algorithms are computationally less expensive and fast, but may not always give the best feature subset. We propose two new FS techniques, Metric Ranked Feature Inclusion (MRFI) and Accuracy Ranked Feature Inclusion (ARFI), which can be used effectively across a variety of learning models. Our proposed algorithms are hybrids of the wrapper and filter methods and follow a two phase process. The second stage behaves as the wrapper part Both MRFI and ARFI share the same feature subset selection technique.
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