Tuesday, March 02, 2021

Journal of Information and Communication Technology (JICT) Vol.20, No.2, April 2021

 
Saroj Ratnoo, Sunil Kumar & Jyoti Vashishtha
 

 
ENSEMBLE META CLASSIFIER WITH SAMPLING AND FEATURE SELECTION FOR DATA WITH IMBALANCE MULTICLASS PROBLEM
1Mohd Shamrie Sainin, 2Rayner Alfred & 3Faudziah Ahmad
1&2Faculty of Computing and Informatics, Universiti Malaysia Sabah, Malaysia
3School of Computing, Universiti Utara Malaysia, Malaysia
 
Corresponding author: shamrie@ums.edu.my; ralfred@ums.edu.my; fudz@uum.edu.my
 
 
ABSTRACT
 
Ensemble learning by combining several single or another ensemble classifier is one of the procedures to solve the imbalance problem in multiclass data. However, this approach is still facing the question of how the ensemble methods obtain their higher performance. In this paper, the investigation is carried out on the design of the ensemble meta classifier with sampling and feature selection for imbalance multiclass data. The specific objectives are 1) to improve the ensemble classifier through data-level approach (sampling and feature selection); 2) to perform experiments on sampling, feature selection, and ensemble classifier model; and 3) to evaluate the performance of the ensemble classifier. To fulfill the objectives, a preliminary data collection of Malaysian plants leaf images was prepared, experimented, and comparing the results. The ensemble design is also tested with another three high imbalance ratio benchmark data. It is found that the design using sampling, feature selection and ensemble classifier method using AdaboostM1 with Random Forest (also an ensemble classifier) provides the improved performance throughout the investigation. The result of this study is important to the ongoing problem of multiclass imbalance where specific structure and its performance can be improved in terms of processing time and accuracy.
 
Keywords: Imbalance, multiclass, ensemble, feature selection, sampling.
 
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AUTOMATION OF QURANIC READINGS GATHERING PROCESS
Majed AbuSafiya
Department of Software Engineering, Al-Ahliyya Amman University, Jordan
 
Corresponding author: majedabusafiya@gmail.com
 
 
ABSTRACT
 
Quran has been the focus of many computer science and computational linguistics researchers. However, little contribution was directed towards Quranic readings. This field focuses on the different ways in which Quranic words can be recited. Quranic Readings Gathering is a recitation process that is well-known by the specialists in this field. In this process, all the possible ways of reading a verse are covered in a compact well-structured manner. To the best of the author's knowledge, no computational automation was cited for this process in literature. In this paper, this process is algorithmically formulated and a developed software system is presented to automate this process. This system takes the Quranic verse text as input and generates a text file containing the detailed gathering of the readings of this verse as done by a specialized reciter. The system was applied to the verses of Surat-Al-Baqara and the output text was carefully validated against authentic books in this field. This system can be the basis for software systems for teaching and vocal construction of quranic readings gathering. This would be useful for students that are interested in Quranic Readings.
 
Keywords: Algorithm, Computational Linguistics, Quran, Quranic Readings Gathering, Tree.
 
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REVIEW OF THE MULTI-OBJECTIVE SWARM INTELLIGENCE OPTIMIZATION ALGORITHMS
Shaymah Akram Yasear & Ku Ruhana Ku-Mahamud
School of Computing, Universiti Utara Malaysia, Malaysia
 
Corresponding author: shayma.akram.yasear@gmail.com, ruhana@uum.edu.my
 
 
ABSTRACT
 
Multi-objective swarm intelligence (MOSI) metaheuristics were proposed to solve multi-objective optimization problems (MOPs) that consists of two or more conflict objectives, in which improving an objective leads to the degradation of the other. The MOSI algorithms are based on the integration of single objective algorithms and multi-objective optimization (MOO) approach. The MOO approaches include scalarization, Pareto dominance, decomposition and indicator-based. In this paper, the status of MOO research and state-of-the-art MOSI algorithms namely, multi-objective particle swarm, artificial bee colony, firefly algorithm, bat algorithm, gravitational search algorithm, grey wolf optimizer, bacterial foraging and moth-flame optimization algorithms have been reviewed. These reviewed algorithms were mainly developed to solve continuous MOPs. The review is based on how the algorithms deal with objective functions using MOO approaches, the benchmark MOPs used in the evaluation and performance metrics. Furthermore, it describes the advantages and disadvantages of each MOO approach and provides some possible future research directions in this area. The results show that several MOO approaches have not been used in most of the proposed MOSI algorithms. Integrating other different MOO approaches may help in developing more effective optimization algorithms, especially in solving complex MOPs. Furthermore, most of the MOSI algorithms have been evaluated using MOPs with two objectives, which clarifies open issues in this research area.
 
Keywords: Optimization, metaheuristic, nature-inspired, Pareto front, population-based.
 
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AN IMPROVED GREY WOLF OPTIMIZATION-BASED LEARNING OF ARTIFICIAL NEURAL NETWORK FOR MEDICAL DATA CLASSIFICATION
Narender Kumar & Dharmender Kumar
Department of Computer Science and Engineering, Guru Jambheshwar University of Science and Technology, India
 
Corresponding author: narenderster@gmail.com; dharmindia24@gmail.com
 
 
ABSTRACT 
 
Grey wolf optimization (GWO) is a recent and popular swarm-based metaheuristic approach. It has been used in numerous fields such as numerical optimization, engineering problems, and machine learning. The different variants of GWO have been developed in the last 5 years for solving optimization problems in diverse fields. Like other metaheuristic algorithms, GWO also suffers from local optima and slow convergence problems, resulted in degraded performance. An adequate equilibrium among exploration and exploitation is a key factor to the success of meta-heuristic algorithms especially for optimization task. In this paper, a new variant of GWO, called inertia motivated GWO (IMGWO) is proposed. The aim of IMGWO is to establish better balance between exploration and exploitation. Traditionally, artificial neural network (ANN) with backpropagation (BP) depends on initial values and in turn, attains poor convergence. The metaheuristic approaches are better alternative instead of BP. The proposed IMGWO is used to train the ANN to prove its competency in terms of prediction. The proposed IMGWO-ANN is used for medical diagnosis task. Some benchmark medical datasets including heart disease, breast cancer, hepatitis, and parkinson's diseases are used for assessing the performance of IMGWO-ANN. The performance measures are described in terms of mean squared errors (MSEs), classification accuracies, sensitivities, specificities, the area under the curve (AUC), and receiver operating characteristic (ROC) curve. It is found that IMGWO outperforms than three popular metaheuristic approaches including GWO, genetic algorithm (GA), and particle swarm optimization (PSO). Results confirmed the potency of IMGWO as a viable learning technique for an ANN.
 
Keywords: Metaheuristic, Medical Diagnosis, Grey Wolf Optimization, Artificial Neural Network, Multilayer Perceptron, Particle Swarm Optimization.
 
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HYPER HEURISTIC EVOLUTIONARY APPROACH FOR CONSTRUCTING DECISION TREE CLASSIFIERS
Saroj Ratnoo, Sunil Kumar & Jyoti Vashishtha
Department of Computer Science and Engineering,Guru Jambheshwar University of Science & Technology, India
 
Corresponding author: skvermacse@gmail.com; ratnoosaroj@gmail.com; jyoti.vst@gmail.com
 
 
ABSTRACT 
 
Decision tree models have earned a special status in predictive modeling since these are considered comprehensible for human analysis and insight. Classification and Regression Tree (CART) algorithm is one of the renowned decision tree induction algorithms to address the classification as well as regression problems. Finding optimal values for the hyper parameters of a decision tree construction algorithm is a challenging issue. While making an effective decision tree classifier with high accuracy and comprehensibility, we need to address the question of setting optimal values for its hyper parameters like the maximum size of the tree, the minimum number of instances required in a node for inducing a split, node splitting criterion and the amount of pruning. The hyper parameter setting influences the performance of the decision tree model. As researchers, we know that no single setting of hyper parameters works equally well for different datasets. A particular setting that gives an optimal decision tree for one dataset may produce a sub-optimal decision tree model for another dataset. In this paper, we present a hyper heuristic approach for tuning the hyper parameters of Recursive and Partition Trees (rpart), which is a typical implementation of CART in statistical and data analytics package R. We employ an evolutionary algorithm as hyper heuristic for tuning the hyper parameters of the decision tree classifier. The approach is named as Hyper heuristic Evolutionary Approach with Recursive and Partition Trees (HEARpart). The proposed approach is validated on 30 datasets. It is statistically proved that HEARpart performs significantly better than WEKA’s J48 algorithm in terms of error rate, F-measure, and tree size. Further, the suggested hyper heuristic algorithm constructs significantly comprehensible models as compared to WEKA’s J48, CART and other similar decision tree construction strategies. The results show that the accuracy achieved by the hyper heuristic approach is slightly less as compared to the other comparative approaches.
 
Keywords: Machine Learning, Evolutionary Algorithms, Hyper Heuristic, Decision Trees, Classification, CART.
 
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