Research on multi-class imbalance from a number of researchers faces obstacles in the form of poor data diversity and a large number of classifiers. The Hybrid Approach Redefinition-Multiclass Imbalance (HAR-MI) method is a Hybrid Ensembles method which is the development of the Hybrid Approach Redefinion (HAR) method. This study has compared the results obtained with the Dynamic Ensemble Selection-Multiclass Imbalance (DES-MI) method in handling multiclass imbalance. In the HAR-MI Method, the preprocessing stage was carried out using the random balance ensembles method and dynamic ensemble selection to produce a candidate ensemble and the processing stages was carried out using different contribution sampling and dynamic ensemble selection to produce a candidate ensemble. This research has been conducted by using multi-class imbalance datasets sourced from the KEEL Repository. The results show that the HAR-MI method can overcome multi-class imbalance with better data diversity, smaller number of classifiers, and better classifier performance compared to a DES-MI method. These results were tested with a Wilcoxon signed-rank statistical test which showed that the superiority of the HAR-MI method with respect to DES-MI method.
Decision-making on conditions that involve many alternatives, many criteria, and many judgments is a difficult thing to do. The difficulty is coupled with assessors who sometimes make decisions in hesitant, unclear, and inconsistent circumstances and each person can provide different judgments. One of the methods that can be used is Hesitant Fuzzy Linguistic Term Sets which is the development of Fuzzy Sets that can make decisions by using Hesitant Fuzzy Sets. Hesitant linguistic term has been introduced for capturing the human way of reasoning using linguistic expressions involving different levels of precision. The integration of Hesitant Fuzzy Linguistic Term Sets with Fuzzy Grid Partition will enhance the ability in the decision making process. This research will discuss the use of Hesitant Fuzzy Linguistic Term Sets method and Fuzzy Grid Partition for best lecturers determination.
Class imbalance and overlapping on multi-class can reduce the performance and accuracy of the classification. Noise must also be considered because it can reduce the performance of classification. With a resampling algorithm and feature selection, this paper proposes a method for improving the performance of hybrid approach redefinition-multi class (HAR-MI). Resampling algorithm can overcome the problem of noise but cannot handle overlapping well. Feature selection is good at dealing with overlapping but can experience a decrease in quality if there is a noise. The HAR-MI approach is a way to deal with multi-class imbalance issues, but it has some drawbacks when dealing with overlapping. The contribution of this paper is to suggest a new approach for dealing with class imbalance, overlapping, and noise in multi-class. This is accomplished by employing minimizing overlapping selection (MOSS) as an ensemble learning algorithm and a preprocessing technique in HAR-MI, as well as employing multi-class combination cleaning and resampling (MC-CCR) as a resampling algorithm at the processing stage. When subjected to overlapping and classifier performance, it is discovered that the proposed method produces good results, as evidenced by higher augmented r-value, class average accuracy, class balance accuracy, multi class g-mean, and confusion entropy.
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