Sukarna Barua, Md. Monirul Islam, Xin Yao, Kazuyuki Murase
Imbalanced learning problems contain an unequal distribution of data samples among different classes and pose a challenge to any classifier as it becomes hard to learn the minority class samples. Synthetic oversampling methods address this problem by generating the synthetic minority class samples t...
Md. Monirul Kabir, Md. Shahjahan, Kazuyuki Murase
Md. Monirul Kabir, Md. Shahjahan, Kazuyuki Murase
Md. Monirul Kabir, Md. Monirul Islam, Kazuyuki Murase
This paper presents a new feature selection (FS) algorithm based on the wrapper approach using neural networks (NNs). The vital aspect of this algorithm is the automatic determination of NN architectures during the FS process. Our algorithm uses a constructive approach involving correlation informat...
Animesh Kumar Paul, Pintu Chandra Shill, Md Rafiqul Islam Rabin, Kazuyuki Murase
Md. Tariqul Islam, Abdus Sattar, Falamaki Amin, Xin Yao et al.
This paper presents a new algorithm, called adaptive merging and growing algorithm (AMGA), in designing artificial neural networks (ANNs). This algorithm merges and adds hidden neurons during the training process of ANNs. The merge operation introduced in AMGA is a kind of a mixed mode operation, wh...
Sukarna Barua, Md. Monirul Islam, Kazuyuki Murase
Mohammad Monirul Islam, Xin Yao, Shahriar Nirjon, Md. Ariful Islam et al.
In this paper, we propose two cooperative ensemble learning algorithms, i.e., NegBagg and NegBoost, for designing neural network (NN) ensembles. The proposed algorithms incrementally train different individual NNs in an ensemble using the negative correlation learning algorithm. Bagging and boosting...
Sukarna Barua, Md. Monirul Islam, Kazuyuki Murase
Md. Musfiqur Rahman Sazal, Sujan Kumar Biswas, Md. Faijul Amin, Kazuyuki Murase
Recognition of Bangla handwritten characters is a difficult but important task for various emerging applications. For better recognition performance, good feature representation of the character images is a primary requirement. In this study, we investigate a recently proposed machine learning appro...
Kalyan Kumar Debnath, Sultan Uddin Ahmed, Md Shahjahan, Kazuyuki Murase
This paper presents a currency recognition system using ensemble neural network (ENN). The individual neural networks (NNs) in an ENN are trained via negative correlation learning. The objective of using negative correlation learning (NCL) is to expertise the individuals on different parts or portio...
Md. Monirul Islam, Abdus Sattar, Mohammed A. Amin, Xin Yao et al.
The generalization ability of artificial neural networks (ANNs) is greatly dependent on their architectures. Constructive algorithms provide an attractive automatic way of determining a near-optimal ANN architecture for a given problem. Several such algorithms have been proposed in the literature an...
Md. Monirul Islam, Kazuyuki Murase
To study the regularity and complexity of autonomous behavior, the flow of sensory information obtained in autonomous mobile robots under various conditions was analyzed as a complex system. Sensory information time series Xn was collected from a miniature mobile robot during free navigation, and pl...
Ayon Sen, Md. Monirul Islam, Kazuyuki Murase, Xin Yao
Using a set of binary classifiers to solve multiclass classification problems has been a popular approach over the years. The decision boundaries learnt by binary classifiers (also called base classifiers) are much simpler than those learnt by multiclass classifiers. This paper proposes a new classi...
Proteek Chandan Roy, Md. Monirul Islam, Kazuyuki Murase, Xin Yao
The number of objectives in many-objective optimization problems (MaOPs) is typically high and evolutionary algorithms face severe difficulties in solving such problems. In this paper, we propose a new scalable evolutionary algorithm, called evolutionary path control strategy (EPCS), for solving MaO...
Md. Faijul Amin, Md. Monirul Islam, Kazuyuki Murase
This paper presents ensemble approaches in single-layered complex-valued neural network (CVNN) to solve real-valued classification problems. Each component CVNN of an ensemble uses a recently proposed activation function for its complex-valued neurons (CVNs). A gradient-descent based learning algori...
Pavan Chakraborty, Farhad Ahmed, Md. Monirul Kabir, Md. Shahjahan et al.
M. M. Rahman, Md. Monirul Islam, Kazuyuki Murase, Xin Yao
Time series forecasting (TSF) has been widely used in many application areas such as science, engineering, and finance. The phenomena generating time series are usually unknown and information available for forecasting is only limited to the past values of the series. It is, therefore, necessary to ...
M. A. H. Akhand, Mahfuza Akter Maria, Md Abdus Samad Kamal, Kazuyuki Murase
Electroencephalography (EEG), despite its inherited complexity, is a preferable brain signal for automatic human emotion recognition (ER), which is a challenging machine learning task with emerging applications. In any automatic ER, machine learning (ML) models classify emotions using the extracted ...
Sudipta Singha, Sk. Imran, M. A., Kazuyuki Murase
Image classification, a complex perceptual task with many real life important applications, faces a major challenge in presence of noise. Noise degrades the performance of the classifiers and makes them less suitable in real life scenarios. To solve this issue, several researches have been conducted...