Search

Text classification (TC) is a crucial practice in case of organizing a vast number of documents. The computational complexity of the TC process is usually high because of the large dimensionality of the feature space. Feature Selection (FS) procedures are used to extract the helpful information from the feature space and results in dimensionality reduction. The development of the FS method that reduces the dimensionality of feature space without compromising the categorization accuracy is desirable. This paper proposes a Self-Inertia Weight Adaptive Particle Swarm Optimization (SIW-APSO) based FS methodology to enhance the performance of text classification systems. SIW- APSO has fast convergence phenomena due to its high search competency and ability to find feature sub-set efficiently. For text classification, the K-nearest neighbors algorithm is used. The experimental analysis shows that the proposed method outperformed the existing state-of-the-art algorithms on the Reuters-21578 data set by achieving 98.60% precision, 96.56% recall, and 97.57% F1 score. [ABSTRACT FROM AUTHOR]

Scanning images and converting the scanned information into digital format is an active research area. Scanning is an automated, fast and efficient process as compared to the traditional data entry, and the resultant converted data is more accurate. Recognizing digits from the scanned images is a challenging task. To address this issue, most of the existing techniques perform multiple individual steps that are localization, segmentation and recognition. Some researchers also focused on adopting a unified approach that combined these three steps for multi-digit recognition of up to five digits. To cope with the modern requirements, a unified multi-digit recognition technique capable of recognizing more than five digits is the need of the hour. Considering this necessity, a unified multi-digit recognition approach is presented in the current study that can recognize sequences up to 18 digits long. The proposed technique is based on a deep convolutional neural network algorithm that performs two basic functions. First, it localizes and extracts the region of interest in the image, and then it performs multi-digit recognition. The proposed algorithm recognizes sequences of up to 18 characters that makes it one of the preferred recognition techniques among the existing algorithms. The proposed technique is compared with state-of-the-art techniques and is proved to be superior and robust. The experiments are performed on two datasets, and overall accuracy up to 98% is achieved. [ABSTRACT FROM AUTHOR]

Feature selection is an important task to improve the classifier's accuracy and to decrease the problem size. A number of methodologies have been presented for feature selection problems using metaheuristic algorithms. In this paper, an improved self-adaptive inertia weight particle swarm optimisation with local search and combined with C4.5 classifiers for feature selection algorithm is proposed. In this proposed algorithm, the gradient base local search with its capacity of helping to explore the feature space and an improved self-adaptive inertia weight particle swarm optimisation with its ability to converge a best global solution in the search space. Experimental results have verified that the SIW-APSO-LS performed well compared with other state of art feature selection techniques on a suit of 16 standard data sets. [ABSTRACT FROM AUTHOR]

As an evolutionary computing technique, particle swarm optimization (PSO) has good global search ability, but the swarm can easily lose its diversity, leading to premature convergence. To solve this problem, an improved self-inertia weight adaptive particle swarm optimization algorithm with a gradient-based local search strategy (SIW-APSO-LS) is proposed. This new algorithm balances the exploration capabilities of the improved inertia weight adaptive particle swarm optimization and the exploitation of the gradient-based local search strategy. The self-inertia weight adaptive particle swarm optimization (SIW-APSO) is used to search the solution. The SIW-APSO is updated with an evolutionary process in such a way that each particle iteratively improves its velocities and positions. The gradient-based local search focuses on the exploitation ability because it performs an accurate search following SIW-APSO. Experimental results verified that the proposed algorithm performed well compared with other PSO variants on a suite of benchmark optimization functions. [ABSTRACT FROM AUTHOR]