Ant colony optimization (ACO) is a well-explored meta-heuristic algorithm, among whose many applications feature selection (FS) is an important one. Most existing versions of ACO are either wrapper ...based or filter based. In this paper, we propose a wrapper-filter combination of ACO, where we introduce subset evaluation using a filter method instead of using a wrapper method to reduce computational complexity. A memory to keep the best ants and feature dimension-dependent pheromone update has also been used to perform FS in a multi-objective manner. Our proposed approach has been evaluated on various real-life datasets, taken from UCI Machine Learning repository and NIPS2003 FS challenge, using K-nearest neighbors and multi-layer perceptron classifiers. The experimental outcomes have been compared to some popular FS methods. The comparison of results clearly shows that our method outperforms most of the state-of-the-art algorithms used for FS. For measuring the robustness of the proposed model, it has been additionally evaluated on facial emotion recognition and microarray datasets.
Recognition of human actions from visual contents is a budding field of computer vision and image understanding. The problem with such a recognition system is the huge dimensions of the feature ...vectors. Many of these features are irrelevant to the classification mechanism. For this reason, in this paper, we propose a novel feature selection (FS) model called cooperative genetic algorithm (CGA) to select some of the most important and discriminating features from the entire feature set to improve the classification accuracy as well as the time requirement of the activity recognition mechanism. In CGA, we have made an effort to embed the concepts of cooperative game theory in GA to create a both-way reinforcement mechanism to improve the solution of the FS model. The proposed FS model is tested on four benchmark video datasets named Weizmann, KTH, UCF11, HMDB51, and two sensor-based UCI HAR datasets. The experiments are conducted using four state-of-the-art feature descriptors, namely HOG, GLCM, SURF, and GIST. It is found that there is a significant improvement in the overall classification accuracy while considering very small fraction of the original feature vector.
Feature Selection (FS) is an important aspect of knowledge extraction as it helps to reduce dimensionality of data. Among the numerous FS algorithms proposed over the years, Gravitational Search ...Algorithm (GSA) is a popular one which has been applied to various domains. However, GSA suffers from the problem of pre-mature convergence which affects exploration leading to performance degradation. To aid exploration, in the present work, we use a clustering technique in order to make the initial population distributed over the entire feature space and to increase the inclusion of features which are more promising. The proposed method is named Clustering based Population in Binary GSA (CPBGSA). To assess the performance of our proposed model, 20 standard UCI datasets are used, and the results are compared with some contemporary methods. It is observed that CPBGSA outperforms other methods in 12 out of 20 cases in terms of average classification accuracy. The relevant codes of the entire CPBGSA model can be found in the provided link: https://github.com/ManosijGhosh/Clustering-based-Population-in-Binary-GSA.
•Exploration ability of BGSA is enhanced through guided initial population creation.•Application of CPBGSA over 20 popular UCI datasets with varying feature dimensions.•Successful comparison of the proposed algorithm with 11 state-of-the-art FS models.
Feature selection methods are used to identify and remove irrelevant and redundant attributes from the original feature vector that do not have much contribution to enhance the performance of a ...predictive model. Meta-heuristic feature selection algorithms, used as a solution to this problem, need to have a good trade-off between exploitation and exploration of the search space. Genetic Algorithm (GA), a popular meta-heuristic algorithm, lacks exploitation capability, which in turn affects the local search ability of the algorithm. Basically, GA uses mutation operation to take care of exploitation which has certain limitations. As a result, GA gets stuck in local optima. To encounter this problem, in the present work, we have intelligently blended the Great Deluge Algorithm (GDA), a local search algorithm, with GA. Here GDA is used in place of mutation operation of the GA. Application of GDA yields a high degree of exploitation through the use of perturbation of candidate solutions. The proposed method is named as Deluge based Genetic Algorithm (DGA). We have applied the DGA on 15 publicly available standard datasets taken from the UCI dataset repository. To show the classifier independent nature of the proposed feature selection method, we have used 3 different classifiers namely K-Nearest Neighbour (KNN), Multi-layer Perceptron (MLP) and Support Vector Machine (SVM). Comparison of DGA has been performed with other contemporary algorithms like the basic version of GA, Particle Swarm Optimisation (PSO), Simulated Annealing (SA) and Histogram based Multi-Objective GA (HMOGA). From the comparison results, it has been observed that DGA performs much better than others in most of the cases. Thus, our main contributions in this paper are introduction of a new variant of GA for FS which uses GDA to strengthen its exploitational ability and application of the proposed method on 15 well-known UCI datasets using KNN, MLP and SVM classifiers.
Contrast enhancement is an important pre-processing task in any Image Analysis (IA) system. In this paper, we formulate the image contrast enhancement problem as an optimization problem where the ...goal is to optimize the pixel intensity values of an input image to obtain a contrast enhanced version of the same. This optimization task is executed by suitably customizing a nature-inspired optimization algorithm called Selfish Herd Optimizer (SHO). The optimization problem is solved using two different solution representations: pixel wise optimization (SHO(direct)) and transformation function based optimization (SHO(transformation)). Moreover, an ablation study is performed to select the most appropriate parameters which can be used in fitness measure for this optimization problem. On experimenting over the popular Kodak image dataset, it has been observed that the proposed methods outperform many existing methods published recently. Further comparisons indicate that the direct approach performs better than its transformation counterpart. This paper further investigates the robustness of SHO(direct) approach by applying it to enhance the degraded document images of H-DIBCO 2018.
Feature selection (FS) is a technique which helps to find the most optimal feature subset to develop an efficient pattern recognition model under consideration. The use of genetic algorithm (GA) and ...particle swarm optimization (PSO) in the field of FS is profound. In this paper, we propose an insightful way to perform FS by amassing information from the candidate solutions produced by GA and PSO. Our aim is to combine the exploitation ability of GA with the exploration capacity of PSO. We name this new model as binary genetic swarm optimization (BGSO). The proposed method initially lets GA and PSO to run independently. To extract sufficient information from the feature subsets obtained by those, BGSO combines their results by an algorithm called average weighted combination method to produce an intermediate solution. Thereafter, a local search called sequential one-point flipping is applied to refine the intermediate solution further in order to generate the final solution. BGSO is applied on 20 popular UCI datasets. The results were obtained by two classifiers, namely,
nearest neighbors (KNN) and multi-layer perceptron (MLP). The overall results and comparisons show that the proposed method outperforms the constituent algorithms in 16 and 14 datasets using KNN and MLP, respectively, whereas among the constituent algorithms, GA is able to achieve the best classification accuracy for 2 and 7 datasets and PSO achieves best accuracy for 2 and 4 datasets, respectively, for the same set of classifiers. This proves the applicability and usefulness of the method in the domain of FS.
A novel meta-heuristic nature-inspired optimization algorithm known as Groundwater Flow Algorithm (GWFA) is proposed in this paper. GWFA is inspired by the movement of groundwater from recharge areas ...to discharge areas. It follows a position update procedure guided by Darcy's law which provides a mathematical framework of groundwater flow. The proposed optimization algorithm has been evaluated on 23 benchmark functions. The significance of the results is statistically validated using the Wilcoxon rank-sum, Friedman, and Kruskal-Walis tests. To prove the robustness of the algorithm, it has been further applied on several standard engineering problems. From these exhaustive experiments, it has been observed that the proposed GWFA can outperform many state-of-the-art optimization algorithms. Source code of this work is available at: https://github.com/Ritam-Guha/GWFA .
In any multi-script environment, handwritten script classification is an unavoidable pre-requisite before the document images are fed to their respective Optical Character Recognition (OCR) engines. ...Over the years, this complex pattern classification problem has been solved by researchers proposing various feature vectors mostly having large dimensions, thereby increasing the computation complexity of the whole classification model. Feature Selection (FS) can serve as an intermediate step to reduce the size of the feature vectors by restricting them only to the essential and relevant features. In the present work, we have addressed this issue by introducing a new FS algorithm, called Hybrid Swarm and Gravitation-based FS (HSGFS). This algorithm has been applied over three feature vectors introduced in the literature recently—Distance-Hough Transform (DHT), Histogram of Oriented Gradients (HOG), and Modified log-Gabor (MLG) filter Transform. Three state-of-the-art classifiers, namely, Multi-Layer Perceptron (MLP), K-Nearest Neighbour (KNN), and Support Vector Machine (SVM), are used to evaluate the optimal subset of features generated by the proposed FS model. Handwritten datasets at block, text line, and word level, consisting of officially recognized 12
Indic
scripts, are prepared for experimentation. An average improvement in the range of 2–5% is achieved in the classification accuracy by utilizing only about 75–80% of the original feature vectors on all three datasets. The proposed method also shows better performance when compared to some popularly used FS models. The codes used for implementing HSGFS can be found in the following Github link:
https://github.com/Ritam-Guha/HSGFS
.
The feature selection process is very important in the field of pattern recognition, which selects the informative features so as to reduce the curse of dimensionality, thus improving the overall ...classification accuracy. In this paper, a new feature selection approach named Memory-Based Histogram-Oriented Multi-objective Genetic Algorithm (M-HMOGA) is introduced to identify the informative feature subset to be used for a pattern classification problem. The proposed M-HMOGA approach is applied to two recently used feature sets, namely Mojette transform and
features. The experimentations are carried out on
,
, and
numeral datasets, which are the three most popular scripts used in the Indian subcontinent. In-house
and
script datasets and Competition on Handwritten Digit Recognition (HDRC) 2013
numeral dataset are used for evaluating our model. Moreover, as proof of robustness, we have applied an innovative approach of using different datasets for training and testing. We have used in-house
and
script datasets for training the model, and the trained model is then tested on Indian Statistical Institute numeral datasets. For
numerals, we have used the HDRC 2013 dataset for training and the Modified National Institute of Standards and Technology dataset for testing. Comparison of the results obtained by the proposed model with existing HMOGA and MOGA techniques clearly indicates the superiority of M-HMOGA over both of its ancestors. Moreover, use of K-nearest neighbor as well as multi-layer perceptron as classifiers speaks for the classifier-independent nature of M-HMOGA. The proposed M-HMOGA model uses only about 45–50% of the total feature set in order to achieve around 1% increase when the same datasets are partitioned for training-testing and a 2–3% increase in the classification ability while using only 35–45% features when different datasets are used for training-testing with respect to the situation when all the features are used for classification.
Multi-objective optimization problems give rise to a set of Pareto-optimal solutions, each of which makes a trade-off among the objectives. When multiple Pareto-optimal solutions are to be ...implemented for different applications as platform-based solutions, a solution principle common to them is highly desired for easier understanding, implementation, and management purposes. In this paper, we propose a systematic search methodology that deviates from finding Pareto-optimal solutions, but finds a set of near Pareto-optimal solutions sharing common principles of a desired structure and still possessing a trade-off among objectives. After proposing the regular evolutionary multi-objective optimization (RegEMO) algorithm, we first demonstrate its working principle on a number of constrained and unconstrained multi-objective test problems. Thereafter, we demonstrate the practical significance of the proposed approach to a number of engineering design problems. Searching for a set of solutions with common principles of desire, rather than theoretical Pareto-optimal solutions without any common structure, is a practically meaningful task and this paper should encourage more such practice-oriented developments of EMO in the near future.
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