A hybrid swarm intelligence algorithm for simultaneous feature selection and clustering

Download

Hasan Geren MS Thesis.pdf

Date

2022-6-20

Author

Geren, Hasan

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

221
views

138
downloads

In this study, we address the feature selection and clustering problems by using a hybrid swarm intelligence approach. We assume that the number of clusters is known, clusters can be of any shape and have different densities, but there are no outliers or noise. The data set may have high dimensionality and redundant features. We propose a swarm intelligence algorithm, namely ACOVNS, which is a hybridization of Ant Colony Optimization (ACO) and Variable Neighborhood Search (VNS). We utilize the ACO mechanisms for exploration and enhance its exploitation capability by combining it with VNS. In addition to pheromone values, we make use of some heuristic information to further improve the performance of the algorithm. In the first part of our study, we use our algorithm with an objective function based on the sum of Euclidean distances to solve the clustering problem. In the second part, we modify the ACOVNS algorithm as F-ACOVNS to perform feature selection and clustering simultaneously. We propose a novel heuristic information that employs the Laplacian Score (LS) and a second pheromone matrix for feature selection. Therefore, the algorithm selects features during clustering by using distinct pheromone matrices and heuristic information. Our proposed algorithms are unique in that ACOVNS is the first hybridization of ACO and VNS for clustering and F-ACOVNS is the first algorithm that uses LS as heuristic information. We compared the performance of ACOVNS with some well-known algorithms on nine real-world data sets. For simultaneous feature selection and clustering, we compared F-ACOVNS with known single and multi-objective algorithms using both real and synthetic data sets.

Subject Keywords

Clustering, Feature selection, Swarm intelligence, Ant colony optimization, Variable neighborhood search

URI

https://hdl.handle.net/11511/98094

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

A new framework of multi-objective evolutionary algorithms for feature selection and multi-label classification of video data Karagoz, Gizem Nur; Yazıcı, Adnan; Dokeroglu, Tansel; Coşar, Ahmet (2020-06-01) There are few studies in the literature to address the multi-objective multi-label feature selection for the classification of video data using evolutionary algorithms. Selecting the most appropriate subset of features is a significant problem while maintaining/improving the accuracy of the prediction results. This study proposes a framework of parallel multi-objective Non-dominated Sorting Genetic Algorithms (NSGA-II) for exploring a Pareto set of non-dominated solutions. The subsets of non-dominated featu...
A memetic algorithm for clustering with cluster based feature selection Şener, İlyas Alper; İyigün, Cem; Department of Operational Research (2022-8) Clustering is a well known unsupervised learning method which aims to group the similar data points and separate the dissimilar ones. Data sets that are subject to clustering are mostly high dimensional and these dimensions include relevant and redundant features. Therefore, selection of related features is a significant problem to obtain successful clusters. In this study, it is considered that relevant features for each cluster can be varied as each cluster in a data set is grouped by different set of fe...
An intelligent multimedia information system for multimodal content extraction and querying Yazıcı, Adnan; Yilmaz, Turgay; Sattari, Saeid; SERT, MUSTAFA; Gulen, Elvan (2018-01-01) This paper introduces an intelligent multimedia information system, which exploits machine learning and database technologies. The system extracts semantic contents of videos automatically by using the visual, auditory and textual modalities, then, stores the extracted contents in an appropriate format to retrieve them efficiently in subsequent requests for information. The semantic contents are extracted from these three modalities of data separately. Afterwards, the outputs from these modalities are fused...
A novel multistart hyper-heuristic algorithm on the grid for the quadratic assignment problem Dokeroglu, Tansel; Coşar, Ahmet (2016-06-01) Hyper-heuristics introduce novel approaches for solving challenging combinatorial optimization problems by operating over a set of low level (meta)-heuristics. This is achieved by an evolutionary selection mechanism that controls and combines the strengths of the low level (meta) -heuristics. In this study, we propose a high-performance MultiStart Hyper-heuristic algorithm (MSH-QAP) on the grid for the solution of the Quadratic Assignment Problem (QAP). MSH-QAP algorithm makes use of state-of:the-art (meta)...
A matheuristic for binary classification of data sets using hyperboxes Akbulut, Derya; İyigün, Cem; Özdemirel, Nur Evin (null; 2018-07-08) In this study, an optimization approach is proposed for the binary classification problem. A Mixed Integer Programming (MIP) model formulation is used to construct hyperboxes as classifiers, minimizing the number of misclassified and unclassified samples as well as overlapping of hyperboxes. The hyperboxes are determined by some lower and upper bounds on the feature values, and overlapping of these hyperboxes is allowed to keep a balance between misclassification and overfitting. A matheuristic, namely Iter...

Citation Formats

H. Geren, “A hybrid swarm intelligence algorithm for simultaneous feature selection and clustering,” M.S. - Master of Science, Middle East Technical University, 2022.