Unsupervised Feature Selection Using Evolutionary Algorithms

Ms. Aishwarya Deshpande, Ms. Sharvari Deshpande, Ms. Monika Doke, Ms. Anagha Chaudhari

Classification is a central problem in the fields of data mining and machine learning. Using a training set of labelled instances, the task is to build a model (classifier) that can be used to predict the class of new unlabelled instances. Data preparation is crucial to the data mining process, and its focus is to improve the fitness of the training data for the learning algorithms to produce more effective classifiers. Searching for the frequent pattern within a specific sequence has become a much needed task in the various sector. Most recent works are based on Apriori algorithm, GSP, MacroVspan etc. techniques. However, frequent pattern mining can be made more efficient. Two widely applied data preparation methods are feature selection and instance selection, which fall under the umbrella of data reduction. Feature selection is selecting a subset of optimal features. Feature selection is being used in high dimensional data reduction and it is being used in several applications like medical, image processing, text mining, etc. Several methods were introduced for unsupervised feature selection. Among those methods some are based on filter approach and some are based on wrapper approach. In the existing work, unsupervised feature selection methods using Genetic Algorithm, Bat Algorithm and Ant Colony Optimization have been introduced. These methods yield better performance for unsupervised feature selection. We will use a novel method to select subset of features from unlabeled data using binary bat algorithm with sum of squared error as the fitness function.