Optimization of cuckoo algorithm (combination of cuckoo algorithm withgenetic algorithm) for feature selection in text classification

Classification systems are methods of grouping and organizing data so that they can be compared withother data. The type of classification system used depends on what the data is intended to measure.Some datasets may use multiple classification systems. Feature selection is the most important step inclassification systems and plays an important role in many fields such as pattern recognition, machinelearning, signal processing and other data mining. The goal of feature selection is to find the smallestsubset of input features and it is widely used in high-dimensional data, such as text processing andclassification, which improves classification performance. Therefore, the high dimensions of the textis the most important problem in text classification. This paper presents a new method, based on thecuckoo optimization method, which finds optimal or semi-optimal solutions in polynomial timecomplexity, and the proposed method is easily implemented using a simple classifier. In order todemonstrate the power of the proposed method, the performance is compared with a geneticalgorithm, cuckoo-based optimization method, chi-square, information gain (IG) and anothercombination of cuckoo and genetic algorithm on the benchmark dataset. The simulation results of thisdataset show the superiority of the proposed feature selection.

Keywords:

feature selection , text mining , meta heuristic algorithm , cuckoooptimization algorithm