Integration of multi-objective PSO based feature selection and node centrality for medical datasets

In the past decades, the rapid growth of computer and database technologies has led to the rapid growth of large-scale medical datasets. On the other, medical applications with high dimensional datasets that require high speed and accuracy are rapidly increasing. One of the dimensionality reduction approaches is feature selection that can increase the accuracy of the disease diagnosis and reduce its computational complexity. In this paper, a novel PSO-based multi objective feature selection method is proposed. The proposed method consists of three main phases. In the first phase, the original features are showed as a graph representation model. In the next phase, feature centralities for all nodes in the graph are calculated, and finally, in the third phase, an improved PSO-based search process is utilized to final feature selection. The results on five medical datasets indicate that the proposed method improves previous related methods in terms of efficiency and effectiveness. © 2020 Elsevier Inc

A hybrid method for recommendation systems based on tourism with an evolutionary algorithm and topsis model

Abstract Recommender systems have been pervasively applied as a technique of suggesting travel recommendations to tourists. Actually, recommendation systems significantly contribute to the decision-making process of tourists. A new approach of recommendation systems in the tourism industry by a combination of the Artificial Bee Colony (ABC) algorithm and Fuzzy TOPSIS is proposed in the present paper. A multi-criteria decision-making method called the Techniques for Order of Preference by Similarity to Ideal Solution (TOPSIS) has been applied for the purpose of optimizing the system. Data were gathered through a 1015 online questionnaire on the Facebook social media site. In the first stage, the TOPSIS model defines a positive ideal solution in the form of a matrix with four columns, which indicates factors that get involved in this study. In the second stage, the ABC algorithm starts to search amongst destinations and recommends the best tourist spot to users

Community detection algorithms in healthcare applications:a systematic review

Abstract Over the past few years, the number and volume of data sources in healthcare databases has grown exponentially. Analyzing these voluminous medical data is both opportunity and challenge for knowledge discovery in health informatics. In the last decade, social network analysis techniques and community detection algorithms are being used more and more in scientific fields, including healthcare and medicine. While community detection algorithms have been widely used for social network analysis, a comprehensive review of its applications for healthcare in a way to benefit both health practitioners and the health informatics community is still overwhelmingly missing. This paper contributes to fill in this gap and provide a comprehensive and up-to-date literature research. Especially, categorizations of existing community detection algorithms are presented and discussed. Moreover, most applications of social network analysis and community detection algorithms in healthcare are reviewed and categorized. Finally, publicly available healthcare datasets, key challenges, and knowledge gaps in the field are studied and reviewed

Graph-based relevancy-redundancy gene selection method for cancer diagnosis

Abstract Nowadays, microarray data processing is one of the most important applications in molecular biology for cancer diagnosis. A major task in microarray data processing is gene selection, which aims to find a subset of genes with the least inner similarity and most relevant to the target class. Removing unnecessary, redundant, or noisy data reduces the data dimensionality. This research advocates a graph theoretic-based gene selection method for cancer diagnosis. Both unsupervised and supervised modes use well-known and successful social network approaches such as the maximum weighted clique criterion and edge centrality to rank genes. The suggested technique has two goals: (i) to maximize the relevancy of the chosen genes with the target class and (ii) to reduce their inner redundancy. A maximum weighted clique is chosen in a repetitive way in each iteration of this procedure. The appropriate genes are then chosen from among the existing features in this maximum clique using edge centrality and gene relevance. In the experiment, several datasets consisting of Colon, Leukemia, SRBCT, Prostate Tumor, and Lung Cancer, with different properties, are used to demonstrate the efficacy of the developed model. Our performance is compared to that of renowned filter-based gene selection approaches for cancer diagnosis whose results demonstrate a clear superiority

An effective explainable food recommendation using deep image clustering and community detection

Abstract In food diet communication domain, images convey important information to capture users’ attention beyond the traditional ingredient content, making it crucial to influence user-decision about the relevancy of a given diet. By using a deep learning-based image clustering method, this paper proposes an Explainable Food Recommendation system that uses the visual content of food to justify their recommendations. n the recommendation system. Especially, a new similarity score based on a tendency measure that quantifies the extent to which user community prefers a given food category is introduced and incorporated in the recommendation. Finally, a rule-based explainability is introduced to enhance transparency and interpretability of the recommendation outcome. Our experiments on a crawled dataset showed that the proposed method enhances recommendation quality in terms of precision, recall, F1, and Normalized Discounted Cumulative Gain (NDCG) by 7.35%, 6.70%, 7.32% and 14.38%, respectively, when compared to other existing methodologies for food recommendation. Besides ablation study is performed to demonstrate the technical soundness of the various components of our recommendation system

Gene selection for microarray data classification via multi-objective graph theoretic-based method

Abstract In recent decades, the improvement of computer technology has increased the growth of high-dimensional microarray data. Thus, data mining methods for DNA microarray data classification usually involve samples consisting of thousands of genes. One of the efficient strategies to solve this problem is gene selection, which improves the accuracy of microarray data classification and also decreases computational complexity. In this paper, a novel social network analysis-based gene selection approach is proposed. The proposed method has two main objectives of the relevance maximization and redundancy minimization of the selected genes. In this method, on each iteration, a maximum community is selected repetitively. Then among the existing genes in this community, the appropriate genes are selected by using the node centrality-based criterion. The reported results indicate that the developed gene selection algorithm while increasing the classification accuracy of microarray data, will also decrease the time complexity

Dual Regularized Unsupervised Feature Selection Based on Matrix Factorization and Minimum Redundancy with application in gene selection

Abstract Gene expression data have become increasingly important in machine learning and computational biology over the past few years. In the field of gene expression analysis, several matrix factorization-based dimensionality reduction methods have been developed. However, such methods can still be improved in terms of efficiency and reliability. In this paper, an innovative approach to feature selection, called Dual Regularized Unsupervised Feature Selection Based on Matrix Factorization and Minimum Redundancy (DR-FS-MFMR), is introduced. The major focus of DR-FS-MFMR is to discard redundant features from the set of original features. In order to reach this target, the primary feature selection problem is defined in terms of two aspects: (1) the matrix factorization of data matrix in terms of the feature weight matrix and the representation matrix, and (2) the correlation information related to the selected features set. Then, the objective function is enriched by employing two data representation characteristics along with an inner product regularization criterion to perform both the redundancy minimization process and the sparsity task more precisely. To demonstrate the proficiency of the DR-FS-MFMR method, a large number of experimental studies are conducted on nine gene expression datasets. The obtained computational results indicate the efficiency and productivity of DR-FS-MFMR for the gene selection task

Local Community Detection Algorithm with Self-defining Source Nodes

Surprising insights in community structures of complex networks have raised tremendous interest in developing various kinds of community detection algorithms. Considering the growing size of existing networks, local community detection methods have gained attention in contrast to global methods that impose a top-down view of global network information. Current local community detection algorithms are mainly aimed to discover local communities around a given node. Besides, their performance is influenced by the quality of the source node. In this paper, we propose a community detection algorithm that outputs all the communities of a network benefiting from a set of local principles and a self-defining source node selection. Each node in our algorithm progressively adjusts its community label based on an even more restrictive level of locality, considering its neighbours local information solely. Our algorithm offers a computational complexity of linear order with respect to the network size. Experiments on both artificial and real networks show that our algorithm gains moreover networks with weak community structures compared to networks with strong community structures. Additionally, we provide experiments to demonstrate the ability of the self-defining source node of our algorithm by implementing various source node selection methods from the literature