What is Feature Selection?
Feature selection in machine learning refers to the classification of data into multiple classes or features. The classification of data involves the decision to remove one class or feature, to add one class or feature, or to retain one class or feature. Essentially, it involves a trade-off between accuracy and speed. In short, the classification of data plays an important role in the training of machine learning algorithms. How is the decision to remove a class or feature made? At the simplest level, a program will be instructed to remove a training data point from a training run. It may choose to drop a random class or feature, or it may choose to retain a random class or feature.
Application of Feature Selection:
While some have argued that dropping a training data point from a training algorithm is too dangerous because it makes the training algorithms too lax, others believe that it is a necessary prerequisite for good ML software. In fact, some current training systems include a feature called the ‘drop dead’ function. This simply instructs the training system to drop a currently valid class or feature from the training data set immediately. Such a feature ensures that the training data set will remain intact. However, it may fail a validation on some inputs or it may not catch all errors.
Positives of Feature Selection
Feature selection in machine learning algorithms can have a positive effect because it allows for the creation of multiple classifiers. Multiple classifiers, when properly implemented, will be able to extract more relevant information from the training data. When multiple classifiers are combined with supervised learning methods, the classification accuracy of the final algorithm improves greatly. Additionally, the classification accuracy will likely be dependent on how accurate the individual classifiers were in the beginning.
Negatives of Feature Selection:
On the flip side, feature selection can have a negative effect because it will likely make the training procedure more difficult. The classifier will be forced to perform too many repetitions of the same task because it cannot differentiate between valid and invalid data. Moreover, the more features that are used, the more time the model has to accumulate and evaluate new data. Thus, it becomes slower and the training process can become more problematic.
One way to decrease the likelihood of having too many features is to choose a limited number of features. Doing so reduces the amount of data that must be processed and that means that the machine learning algorithm will only have to search for relevant features once. This way, the classification accuracy will be significantly better. This is especially beneficial if the training data is not very complex.
Another consideration to keep in mind is the number of independent variables. If a program is capable of distinguishing among multiple independent variables, then it is probably possible to achieve a relatively lower cost of implementation. However, the drawback is that a large number of classifiers will be needed to make this feasible. Also, when relying on a single variable, the accuracy will likely diminish as the number of classes increases.
One way to reduce the potential of a classification mistake is to ensure that all the required information is provided during training. If one can provide all relevant information during the training session, then the classifier will be able to quickly recognize the valid input and discard invalid data. As mentioned earlier, the importance of the significance of features largely depends on the type of learning being performed. In addition, the training data distribution should be regular so that the classifier will learn to filter out irrelevant data and only concentrate on the useful ones. Such regularities will make it easier for the machine learning algorithm to determine the accuracy of the classifier’s outputs.
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