Incremental DC optimization algorithm for large-scale clusterwise linear regression
- Bagirov, Adil, Taheri, Sona, Cimen, Emre
An incremental nonsmooth optimization algorithm for clustering using L1 and L∞ norms
- Ordin, Burak, Bagirov, Adil, Mohebi, Ehsam
Clusterwise support vector linear regression
- Joki, Kaisa, Bagirov, Adil, Karmitsa, Napsu, Mäkelä, Marko, Taheri, Sona
Cyberattack triage using incremental clustering for intrusion detection systems
- Taheri, Sona, Bagirov, Adil, Gondal, Iqbal, Brown, Simon
- Bagirov, Adil, Taheri, Sona, Karmitsa, Napsu
- Bagirov, Adil, Gaudioso, Manlio, Karmitsa, Napsu, Mäkelä, Marko, Taheri, Sona
- Bagirov, Adil, Gaudioso, Manlio, Karmitsa, Napsu, Mäkelä, Marko, Taheri, Sona
Missing value imputation via clusterwise linear regression
- Karmitsa, Napsu, Taheri, Sona, Bagirov, Adil, Makinen, Pauliina
In this paper a new method of preprocessing incomplete data is introduced. The method is based on clusterwise linear regression and it combines two well-known approaches for missing value imputation: linear regression and clustering. The idea is to approximate missing values using only those data points that are somewhat similar to the incomplete data point. A similar idea is used also in clustering based imputation methods. Nevertheless, here the linear regression approach is used within each cluster to accurately predict the missing values, and this is done simultaneously to clustering. The proposed method is tested using some synthetic and real-world data sets and compared with other algorithms for missing value imputations. Numerical results demonstrate that the proposed method produces the most accurate imputations in MCAR and MAR data sets with a clear structure and the percentages of missing data no more than 25%
New gene selection algorithm using hypeboxes to improve performance of classifiers
- Bagirov, Adil, Mardaneh, Karim
Partial undersampling of imbalanced data for cyber threats detection
- Moniruzzaman, Md, Bagirov, Adil, Gondal, Iqbal
Prediction of gold-bearing localised occurrences from limited exploration data
- Grigoryev, Igor, Bagirov, Adil, Tuck, Michael
The non-smooth and bi-objective team orienteering problem with soft constraints
- Estrada-Moreno, Alejandro, Ferrer, Albert, Juan, Angel, Panadero, Javier, Bagirov, Adil
A difference of convex optimization algorithm for piecewise linear regression
- Bagirov, Adil, Taheri, Sona, Asadi, Soodabeh
A sharp augmented Lagrangian-based method in constrained non-convex optimization
- Bagirov, Adil, Ozturk, Gurkan, Kasimbeyli, Refail
A simulated annealing-based maximum-margin clustering algorithm
- Seifollahi, Sattar, Bagirov, Adil, Borzeshi, Ehsan, Piccardi, Massimo
- Bagirov, Adil, Taheri, Sona, Bai, Fusheng, Wu, Zhiyou
Multi-source cyber-attacks detection using machine learning
- Taheri, Sona, Gondal, Iqbal, Bagirov, Adil, Harkness, Greg, Brown, Simon, Chi, Chihung
A comparative assessment of models to predict monthly rainfall in Australia
- Bagirov, Adil, Mahmood, Arshad
A server side solution for detecting webInject : A machine learning approach
- Moniruzzaman, Md, Bagirov, Adil, Gondal, Iqbal, Brown, Simon
Clustering in large data sets with the limited memory bundle method
- Karmitsa, Napsu, Bagirov, Adil, Taheri, Sona
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