Forest Optimization Algorithm Research Articles

A novel bio-inspired hybrid multi-filter wrapper gene selection method with ensemble classifier for microarray data.

Microarray technology is known as one of the most important tools for collecting DNA expression data. This technology allows researchers to investigate and examine types of diseases and their origins. However, microarray data are often associated with a small sample size, a significant number of genes, imbalanced data, etc., making classification models inefficient. Thus, a new hybrid solution based on a multi-filter and adaptive chaotic multi-objective forest optimization algorithm (AC-MOFOA) is presented to solve the gene selection problem and construct the Ensemble Classifier. In the proposed solution, a multi-filter model (i.e., ensemble filter) is proposed as preprocessing step to reduce the dataset's dimensions, using a combination of five filter methods to remove redundant and irrelevant genes. Accordingly, the results of the five filter methods are combined using a voting-based function. Additionally, the results of the proposed multi-filter indicate that it has good capability in reducing the gene subset size and selecting relevant genes. Then, an AC-MOFOA based on the concepts of non-dominated sorting, crowding distance, chaos theory, and adaptive operators is presented. AC-MOFOA as a wrapper method aimed at reducing dataset dimensions, optimizing KELM, and increasing the accuracy of the classification, simultaneously. Next, in this method, an ensemble classifier model is presented using AC-MOFOA results to classify microarray data. The performance of the proposed algorithm was evaluated on nine public microarray datasets, and its results were compared in terms of the number of selected genes, classification efficiency, execution time, time complexity, hypervolume indicator, and spacing metric with five hybrid multi-objective methods, and three hybrid single-objective methods. According to the results, the proposed hybrid method could increase the accuracy of the KELM in most datasets by reducing the dataset's dimensions and achieve similar or superior performance compared to other multi-objective methods. Furthermore, the proposed Ensemble Classifier model could provide better classification accuracy and generalizability in the seven of nine microarray datasets compared to conventional ensemble methods. Moreover, the comparison results of the Ensemble Classifier model with three state-of-the-art ensemble generation methods indicate its competitive performance in which the proposed ensemble model achieved better results in the five of nine datasets.Supplementary InformationThe online version contains supplementary material available at 10.1007/s00521-021-06459-9.

An Optimal Feature Selection with Ensemble Based Classification of Sentiments Using Forest Optimization Algorithm for Product Review

An Optimal Feature Selection with Ensemble Based Classification of Sentiments Using Forest Optimization Algorithm for Product Review

Comparative Assessment of Forest Optimization with Deep Ensembling Technique for Human Activity Recognition based on Data Collected from Wearable Devices

Aim: Improving the performance of Human Activity Recognition based on information sensed by wearable devices. Materials and methods: In this study we have considered two groups namely forest optimization with sample size of 1408 and deep ensemble techniques with sample size 1408 (Kane, Phar, and BCPS n.d.). Accuracy was computed for the dataset size of 9673 to recognise six various human activities (walking, jogging, standing, upstairs, sitting, downstairs). Result: It was observed that the forest optimization algorithm obtains accuracy as 96% and loss as 14%. Forest optimization technique appears to have better significance than deep ensemble technique with value of p=0.000. Conclusion: The result proves that forest optimization approaches with varying seed value have significant improvement in human activity recognition.

Forest Optimization Algorithm Implementation using Sphere Mathematical Function

Forest Optimization Algorithm (FOA), a recent evolutionary algorithm suitable for continuous nonlinear optimization problems. It is inspired by a few trees in the forest that can last for several decades while other trees can only live for a short time. In FOA, the tree seeding technique is simulated so that certain seeds fall directly under the leaves, while others are dispersed over a large area by natural processes and animals that feed on the seeds or fruits. In this paper, we used the sphere mathematical function to implement FOA as a step-by-step process, and the iteration-based results are displayed. The findings of the experiments demonstrated that FOA performed well in certain data sets from the UCI repository..

A novel multi-objective forest optimization algorithm for wrapper feature selection

Feature selection is one of the important techniques of dimensionality reduction in data preprocessing because datasets generally have redundant and irrelevant features that adversely affect the performance and complexity of classification models. Feature selection has two main objectives, i.e., reducing the number of features and increasing classification performance due to its inherent nature. In this paper, we propose a multi-objective feature selection algorithm based on forest optimization algorithm (FOA) using the archive, grid, and region-based selection concepts. For this purpose, two versions of the proposed algorithm are developed using continuous and binary representations. The performance of the proposed algorithms is investigated on nine UCI datasets and two microarray datasets. Next, the obtained results are compared with seven traditional single-objective and five multi-objective methods. Based on the results, both proposed algorithms have reached the same performance or even outperformed the single-objective methods. Compared with other multi-objective algorithms, MOFOA with continuous representation has managed to reduce the classification error in most cases by selecting less number of features than other methods.

Advanced SLM scheme based on discrete forest optimization algorithm for PAPR minimization in UFMC waveform

Inherent multicarrier transmission mechanism of the universal filtered multicarrier (UFMC) waveform engenders the problem of high peak-to-average power ratio (PAPR). Since it is impossible for a nonlinear high power amplifier (HPA) to execute a distortionless amplification unless the PAPR of transmission signal is below an acceptable level, eliminating the aforementioned PAPR drawback in UFMC waveform is so critical for smooth communication. With this in mind, we developed a new selective mapping (SLM) scheme based on discrete forest optimization algorithm (DFOA) for the UFMC waveform. The related scheme was created by embedding the DFOA into the conventional SLM with the intention of optimizing the values of phase factors by which the phase rotation process is carried out in frequency domain to reduce the PAPR of eventual time domain signal attained from the SLM output. It is confirmed via the simulations that, remarkable PAPR improvements are achieved through the DFOA-SLM scheme in the UFMC signal thanks to the DFOA-supported search for the optimal sequence of phase factors instead of classical random search strategy inherent in the conventional SLM method.

A New Hybrid Optimization Algorithm for the Optimal Allocation of Goods in Shop Shelves

In retail operation management, shelf space allocation problem is an important problem that affects profitability. Various researches have demonstrated that the shelf space allocation of a product affects that product's sales. The decision that how much of which product, where and when should be placed on shelves is a critical issue in retail operation management. In this paper a new hybrid meta-heuristic algorithm based on forest optimization algorithm (FOA) and simulated annulling (SA) is presented to address the shop shelf allocation problem. To apply FOA for shelf space allocation problem, the basic arithmetic operators of FOA have been modified regarding the characteristics of this problem and FOA is improved by SA. Results obtained from an expensive experimental phase show the better performance of the proposed algorithm in comparison with other presented algorithm from the literature. Also, results show the suitability and benefits of the proposed algorithm in finding high-quality solutions and robustness.

WITHDRAWN: An evolutionary approach to load balancing using forest optimization algorithm

Abstract Cloud computing can be described as a distributed computing framework that provides computational facilities, where information and assets are recovered from cloud service providers via the web, by means of a well-framed online application. But resource sharing often leads to unavailability of resources, resulting in a deadlock situation. One approach to avoid this is by disseminating the workload uniformly between the encumbered and idle machines. This is called Load Balancing. The aim of doing this is to reduce the average response time and maximize resource utilization. Forest Optimization Algorithm (FOA) is based on governance of trees in the forest and survival of distinct trees. These trees have proper geological developing conditions. The proposed Algorithm is an attempt to find these distinguished solutions from the pool to avoid starvation of the tasks, at the same time improving the average response time by utilizing the process of seed dispersal in forests.

DIAGNOSIS OF CORONARY BLOCKAGE OF ARTERY USING MRI/CTA IMAGES THROUGH ADAPTIVERANDOM FOREST OPTIMIZATION

Heart attacks and diseases are normally observed due lack of Real time-Fast diagnosis. In that the fundamental and main cause is troubles in coronary blockage of artery (CBA).There are many ways to diagnosis this issue, but it takes more time and complexity function, the main purpose of this work is to give an easy technique for Heart surgery clinical purpose with accurate results. For this consider the open heart surgery CTA images are selected as experimental elements. Here for Accurate diagnosis and operation, clear MRI/CTA images of heart are required i.e., coronary tomography angiography (CTA) images are collected from real time clinical centers. Fast and accurate detections are possible only with the help of image processing techniques (IMT) through artificial intelligence algorithms (AIA). Random forest optimization algorithm is used for taking decisions via feature extraction and classification. Total work consists of 3 steps i.e., first step is loading the image through segmentation, 2nd step is preprocessing, Here the selected particular MRI/CTA heart images are feature extracted and in 3rd step is taking decision by using Adaptive Random Forest algorithm (ARF). Therefore this application achieves accuracy is 98.74%, Precision 97.81 ,Recall 98.14 , F1 score 93.87 PSNR 57.26 CC 0.0987 Sensitivity 98.45, so this work is compete with present technologies and very useful at fast & accurate diagnosis center for Heart patients.

Feature Selection Using Improved Forest Optimization Algorithm

Feature selection is one of the hottest topics in the field of machine learning and data mining. In 2016, the feature selection using forest optimization algorithm (FSFOA) was proposed, which had a better classification performance and dimensionality reduction ability. However, there are some shortcomings in FSFOA. Feature Selection using Improved Forest Optimization Algorithm (FSIFOA) is proposed in this article, which aims at solving the problems of FSFOA during the stages of random initialization, forming the candidate population and updating the best tree. FSIFOA uses the Pearson correlation coefficient and the L1 regularization method to replace the random initialization strategy in the initialization stage, uses the method of separating good and bad trees and filling the quantity gap between them to solve the problem of category imbalance in the candidate population generation stage, adds trees of the same precision but different dimension compared with the best tree to the forest in the update stage. In experiment, the new algorithm uses the same data and parameters as the traditional algorithm to test the small, medium and large dimensional data respectively. The results of the experiments show that the new algorithm can improve the classification accuracy of classifiers and increase the dimension reduction ratio compared with the traditional algorithms in the medium and large dimension data set.

A new Feature Reduction Algorithm Based on Fuzzy Rough Relation for the Multi-label Classification

The paper aims to improve the multi-label classification performance using the feature reduction technique. According to the determination of the dependency among features based on fuzzy rough relation, features with the highest dependency score will be retained in the reduction set. The set is subsequently applied to enhance the performance of the multi-label classifier. We investigate the effectiveness of the proposed model againts the baseline via time complexity.
 Keywords: 
 Fuzzy rough relation, label-specific feature, feature reduction set
 References
 [1] Richard Jensen, Chris Cornelis, Fuzzy-Rough Nearest Neighbor Classification and Prediction. Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing, 2011, 310-319.
 [2] Y.H. Qian, Q. Wang, H.H. Cheng, J.Y. Liang, C.Y. Dang, Fuzzy-Rough feature selection accelerator, Fuzzy Sets Syst. 258 (2014) 61-78.
 [3] Quang-Thuy Ha, Thi-Ngan Pham, Van-Quang Nguyen, Minh-Chau Nguyen, Thanh-Huyen Pham, Tri-Thanh Nguyen, A New Text Semi-supervised Multi-label Learning Model Based on Using the Label-Feature Relations, International Conference on Computational Collective Intelligence, LNAI 11055, Springer, 2018, pp. 403-413.
 [4] Daniel Kostrzewa, Robert Brzeski, The data Dimensionality Reduction and Feature Weighting in the Classification Process Using Forest Optimization Algorithm, ACIIDS, 2019, pp. 97-108.
 [5] Nele Verbiest, Fuzzy Rough and Evolutionary Approaches to Instance Selection, PhD Thesis, Ghent University, 2014.
 [6] Y. Yu, W. Pedrycz, D.Q. Miao, Multi-label classification by exploiting label correlations, Expert syst, Appl. 41 (2014) 2989-3004.
 [7] M.L. Zhang, LIFT: Multi-label learning with label-specific features, IEEE Trans, Pattern Anal, Mach, Intell 37 (2015) 107-120.
 [8] Suping Xu, Xibei Yang, Hualong Yu, Dong-Jun Yu, Jingyu Yang, Eric CC Tsang, Multi-label learning with label-specific feature reduction, Knowledge-Based Systems 104 (2016) 52-61. https://doi.org/10.1080/24751839.2017.1364925.
 [9] Thi-Ngan Pham, Van-Quang Nguyen, Van-Hien Tran, Tri-Thanh Nguyen, Quang-Thuy Ha, A Semi-supervised multi-label classification framework with feature reduction and enrichment, Journal of Information and Telecommunication 1(4) (2017) 305-318.
 [10] M. Ghaemi, M.R. Feizi-Derakhshi, Feature selection using forest optimization algorithm, Pattern Recognition 60 (2016) 121-129.
 [11] M.L. Zhang, Z.H. Zhou, ML-KNN: A lazy learning approach to multi-label learning, Pattern Recognition 40 (2007) 2038-2048.
 [12] M.Z. Ahmad, M.K. Hasan, A New Approach for Computing Zadeh's Extension Principle, MATEMATIKA. 26(1) (2010) 71-81.
 [13] Richard Jensen, Neil Mac Parthaláin and Qiang Shen. Fuzzy-rough data mining (using the Weka data mining suite), A Tutorial, IEEE WCCI 2014, Beijing, China, July 6, 2014.
 [14] D. Dubois, H. Prade, Rough fuzzy sets and fuzzy rough sets, Int. J. Gen. Syst. 17 (1990) 191-209.

Clustering based feature selection using Partitioning Around Medoids (PAM)

High-dimensional data contains a large number of features. With many features, high dimensional data requires immense computational resources, including space and time. Several studies indicate that not all features of high dimensional data are relevant to classification result. Dimensionality reduction is inevitable and is required due to classifier performance improvement. Several dimensionality reduction techniques were carried out, including feature selection techniques and feature extraction techniques. Sequential forward feature selection and backward feature selection are feature selection using the greedy approach. The heuristics approach is also applied in feature selection, using the Genetic Algorithm, PSO, and Forest Optimization Algorithm. PCA is the most well-known feature extraction method. Besides, other methods such as multidimensional scaling and linear discriminant analysis. In this work, a different approach is applied to perform feature selection. Cluster analysis based feature selection using Partitioning Around Medoids (PAM) clustering is carried out. Our experiment results showed that classification accuracy gained when using feature vectors' medoids to represent the original dataset is high, above 80%.

Improving the performance of video Collaborative Filtering Recommender Systems using Optimization Algorithm

The growth amount of information on the Web makes it difficult for many web users to make decision and choose either information or goods. Thus, a recommender system is an approach that helps users to obtain their needs according to her/his preference within a massive amount of information rapidly without waste of time. The main advantage of using a recommender system in any online shopping or social media like Amazon, Netflix and Facebook is to increase the percentage of overall profits, customer satisfaction and retention. In this paper, we introduce an approach to increase the accuracy and to improve the performance of collaborative filtering recommender system. In this paper a hybrid approach is proposed to improve the performance of video collaborative filtering recommender system based on clustering and evolutionary algorithm. Proposed approach combines k-means clustering algorithm and two different evolutionary algorithms which are Accelerated Particle Swarm Optimization Algorithm (APSO) and Forest Optimization Algorithm (FOA). The main aim of this paper is to increase the accuracy of recommendation of user-based collaborative filtering video recommender system. Evaluation and computational results on the MovieLens dataset show that the proposed method has a better performance than the other related methods.

A novel filter-wrapper hybrid gene selection approach for microarray data based on multi-objective forest optimization algorithm

A novel filter-wrapper hybrid gene selection approach for microarray data based on multi-objective forest optimization algorithm

Forest optimization algorithm‐based feature selection using classifier ensemble

AbstractFeatures selection is the process of choosing the relevant subset of features from the high‐dimensional dataset to enhance the performance of the classifier. Much research has been carried out in the present world for the process of feature selection. Algorithms such as Naïve Bayes (NB), decision tree, and genetic algorithm are applied to the high‐dimensional dataset to select the relevant features and also to increase the computational speed. The proposed model presents a solution for selection of features using ensemble classifier algorithms. The proposed algorithm is the combination of minimum redundancy and maximum relevance (mRMR) and forest optimization algorithm (FOA). Ensemble‐based algorithms such as support vector machine (SVM), K‐nearest neighbor (KNN), and NB is further used to enhance the performance of the classifier algorithm. The mRMR‐FOA is used to select the relevant features from the various datasets and 21% to 24% improvement is recorded in the feature selection. The ensemble classifier algorithms further improves the performance of the algorithm and provides accuracy of 96%.

A new optimal gene selection approach for cancer classification using enhanced Jaya-based forest optimization algorithm

In microarray experiments, the sample size is considerably smaller than that of the feature size, thereby imposing the curse of dimensionality problem. To resolve this issue, evolutionary algorithms are often utilized. In this paper, a novel framework for feature selection and classification of the microarray data is presented. Initially, a statistical filter, namely ANOVA, is used to select the relevant genes (features) from the original set of genes. Then, an evolutionary wrapper-based approach utilizing the principles of enhanced Jaya (EJaya) algorithm and forest optimization algorithm (FOA) is proposed to find the optimal set of genes from the previously selected genes. The main objective of using EJaya is to tune the two important parameters, namely local seeding changes and global seeding changes of FOA. During the selection of the optimal set of genes, support vector machine is employed as a classifier to classify the microarray data. To perform a comprehensive experimental study, the proposed method is tested on both binary-class and multi-class microarray datasets. From the extensive result analysis, it has been observed that the proposed technique achieves better classification accuracy with considerably less number of features than that of the benchmark schemes.

Ensemble Based Classification of Sentiments Using Forest Optimization Algorithm

Feature subset selection is a process to choose a set of relevant features from a high dimensionality dataset to improve the performance of classifiers. The meaningful words extracted from data forms a set of features for sentiment analysis. Many evolutionary algorithms, like the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), have been applied to feature subset selection problem and computational performance can still be improved. This research presents a solution to feature subset selection problem for classification of sentiments using ensemble-based classifiers. It consists of a hybrid technique of minimum redundancy and maximum relevance (mRMR) and Forest Optimization Algorithm (FOA)-based feature selection. Ensemble-based classification is implemented to optimize the results of individual classifiers. The Forest Optimization Algorithm as a feature selection technique has been applied to various classification datasets from the UCI machine learning repository. The classifiers used for ensemble methods for UCI repository datasets are the k-Nearest Neighbor (k-NN) and Naïve Bayes (NB). For the classification of sentiments, 15–20% improvement has been recorded. The dataset used for classification of sentiments is Blitzer’s dataset consisting of reviews of electronic products. The results are further improved by ensemble of k-NN, NB, and Support Vector Machine (SVM) with an accuracy of 95% for the classification of sentiment tasks.

Digital mammogram classification using 2D-BDWT and GLCM features with FOA-based feature selection approach

This paper proposes an improved computer-aided diagnosis model to identify mammographic images as normal or abnormal, and further, benign or malignant. The proposed scheme employs all the steps associated with other classification schemes; however, the contribution of the suggested scheme is fourfold. Initially, a fusion-based feature extraction method is employed to obtain the features using a combination of 2-D block discrete wavelet transform (2D-BDWT) and gray-level co-occurrence matrix (GLCM). Next, principal component analysis (PCA) is utilized to reduce the large dimension of the feature vector. Furthermore, to select the most optimal features from the reduced set of features, forest optimization algorithm (FOA) is used. The FOA-based feature selection algorithm is utilized as a wrapper-based technique which includes both feature selection and classification. In the proposed framework, several classifiers, namely support vector machine (SVM), k-nearest neighbor (k-NN), and decision tree (C4.5), are applied. The proposed method is compared with the benchmark schemes on two standard datasets, namely Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM). Simulation results and analysis confirm that the proposed scheme brings potential improvements with respect to classification accuracy, sensitivity, specificity, area under curve, F-score, and Matthews correlation coefficient. The classification accuracy is measured with respect to normal versus abnormal and further, benign versus malignant. The proposed scheme with different combinations of classifiers, namely 2D-BDWT + GLCM + PCA + FOA + SVM, 2D-BDWT + GLCM + PCA + FOA + k-NN, and 2D-BDWT + GLCM + PCA + FOA + C4.5, achieves a maximum classification accuracy of 100% for both the MIAS and DDSM datasets.

Multi-modal forest optimization algorithm

Multi-modal optimization algorithms are one of the most challenging issues in the field of optimization. Most real-world problems have more than one solution; therefore, the potential role of multi-modal optimization algorithms is rather significant. Multi-modal problems consider several global and local optima. Therefore, during the search process, most of the points should be detected by the algorithm. The forest optimization algorithm has been recently introduced as a new evolutionary algorithm with the capability of solving unimodal problems. This paper presents the multi-modal forest optimization algorithm (MMFOA), which is constructed by applying a clustering technique, based on niching methods, to the unimodal forest optimization algorithm. The MMFOA operates by dividing the population of the forest into subpopulations to locate existing local and global optima. Subpopulations are generated by the Basic Sequential Algorithmic Scheme with a radius neighborhood. As population size is self-adaptive in MMFOA, population size can be increased in functions with too many local and global optima. The proposed algorithm is evaluated by a set of multi-modal benchmark functions. The experiment results show that not only is the population size low, but also that the convergence speed is high, and that the algorithm is efficient in solving multi-modal problems.

Feature Selection with a Local Search Strategy Based on the Forest Optimization Algorithm

Feature selection has been widely used in data mining and machine learning. Its objective is to select a minimal subset of features according to some reasonable criteria so as to solve the original task more quickly. In thi... | Find, read and cite all the research you need on Tech Science Press