Abstract
In nature, aquatic ecosystems play a very important aspect. River valleys, wetlands, and water reservoirs are territories for various species of vegetation and wildlife. The prediction of these species is very important for natural resource planning. In this work, a publicly available UCI dataset containing extracted features from satellite imagery is used to classify the presence of newt-amphibians. We convert this multi-class classification problem to the binary classification problem. The transformation leads to being unbalanced classification problem. For the unbalanced classification, in the original form, most machine learning techniques give biased classification results, and their results are inclined in favor of the majority class. We use genetic programming with a newly proposed Euclidean distance and weight-based (EDWB) fitness function to resolve this problem. The result outcomes are compared with original work, support vector machine (SVM), and GP with the standard fitness function. The proposed approach achieves better results than the original work, SVM, and compared GP methods.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Baker J, Beebee T, Buckley J, Gent T, Orchard D (2011) Amphibian habitat management handbook. Amphibian and reptile conservation, Bournemouth
Gardner TA, Barlow J, Peres CA (2007) Paradox, presumption and pitfalls in conservation biology: the importance of habitat change for amphibians and reptiles. Biol Conserv 138(1–2):166–179
Blachnik M, Sołtysiak M, Dąbrowska D (2019) Predicting presence of amphibian species using features obtained from gis and satellite images. ISPRS Int J Geo Inf 8(3):123
Kumar A, Kakkar A, Majumdar R, Singh Baghel A (2015) Spatial data mining: recent trends and techniques. In: 2015 international conference on computer and computational sciences (ICCCS), pages 39–43. IEEE
Mojaddadi H, Pradhan B, Nampak H, Ahmad N, Ghazali AHB (2017) Ensemble machine-learning-based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and gis. Geomatics Natl Hazards Risk 8(2):1080–1102
Kumar A, Sinha N (2020) Classification of forest cover type using random forests algorithm. In: Advances in data and information sciences. Springer, pages 395–402
Avand M, Moradi H et al (2020) Using machine learning models, remote sensing, and gis to investigate the effects of changing climates and land uses on flood probability. J Hydrol, page 125663
Kumar A, Choudhary T (2021) A machine learning approach for the land type classification. In: Innovations in electrical and electronic engineering. Springer
Dua D, Graff C (2017) UCI machine learning repository. URL https://archive.ics.uci.edu/ml/datasets/Amphibians
Beyan C, Fisher R (2015) Classifying imbalanced data sets using similarity based hierarchical decomposition. Pattern Recogn 48(5):1653–1672
Pouyanfar S, Chen S-C (2017) Automatic video event detection for imbalance data using enhanced ensemble deep learning. Int J Semantic Comput 11(01):85–109
Zhu M, Xia J, Jin X, Yan M, Cai G, Yan J, Ning G (2018) Class weights random forest algorithm for processing class imbalanced medical data. IEEE Access 6:4641–4652
Galar M, Fernández A, Barrenechea E, Herrera F (2013) Eusboost: enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling. Pattern Recogn 46(12):3460–3471
Chawla NV, Bowyer KW, Hall LO, Philip W (2002) Kegelmeyersynthetic minority over-sampling technique. Smote J Artif Intell Res 16:321–357
Pei W, Xue B, Shang L, Zhang M (2020) Genetic programming for development of cost-sensitive classifiers for binary high-dimensional unbalanced classification. Appl Soft Comput, page 106989
Mostafizur Rahman M, Davis DN (2013) Addressing the class imbalance problem in medical datasets. Int J Mach Learn Comput 3(2):224
Tahir MAUH, Asghar S, Manzoor A, Asim Noor M (2019) A classification model for class imbalance dataset using genetic programming. IEEE Access 7:71013–71037
Koza JRGP (1992) On the programming of computers by means of natural selection. Genetic Programming
Kumar A, Sinha N, Bhardwaj A (2020) A novel fitness function in genetic programming for medical data classification. J Biomed Inform, page 103623
Asthana M, Gupta KD, Kumar A (2020) Test suite optimization using lion search algorithm. In: Ambient communications and computer systems. Springer, pages 77–90
Koza JR (2010) Human-competitive results produced by genetic programming. Genetic Program Evolvable Mach 11(3–4):251–284
Kumar A, Nagar R, Singh Baghel A (2014) A genetic algorithm approach to release planning in agile environment. In: 2014 international conference on information systems and computer networks (ISCON). IEEE, pages 118–122
Tran B, Xue B, Zhang M (2016) Genetic programming for feature construction and selection in classification on high-dimensional data. Memetic Comput 8(1):3–15
Cuadros-Rodríguez L, Pérez-Castaño E, Ruiz-Samblás C (2016) Quality performance metrics in multivariate classification methods for qualitative analysis. TrAC Trends Anal Chem 80:612–624
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Kumar, A., Sinha, N., Bhardwaj, A. (2022). Predicting the Presence of Newt-Amphibian Using Genetic Programming. In: Tiwari, S., Trivedi, M.C., Kolhe, M.L., Mishra, K., Singh, B.K. (eds) Advances in Data and Information Sciences. Lecture Notes in Networks and Systems, vol 318. Springer, Singapore. https://doi.org/10.1007/978-981-16-5689-7_19
Download citation
DOI: https://doi.org/10.1007/978-981-16-5689-7_19
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-5688-0
Online ISBN: 978-981-16-5689-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)