Regularization Techniques to Improve the Stability and Accuracy of MLC Algorithm - Usman Sudibyo; Noor Ageng Setyanto; Ahmad Wahid Kurniawan; Carissa Devina Usman (Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 2026)
Maximum Likelihood Classification (MLC) is a classification algorithm that has important applications in the fields of image processing and remote sensing. No use of MLC was found in other fields. MLC assumes that data comes from a certain probability distribution (for example, a normal distribution), which may be too simple to describe complex data or have a non-normal distribution. This can lead to poor performance in situations where distribution assumptions are not met. That is why in various literatures there is no use of MLC for classification problems other than remote sensing. We propose a regularization technique to reduce distribution assumption errors in MLC called Regularization on maximum likelihood classification (RMLC). Regularization techniques are integrated into the covariance matrix, where regularization can make the data variance larger or smaller than the actual variance. This technique can also overcome singularities in the covariance matrix, non-Gaussian data, and data containing outliers. Experimental results on 13 public datasets show a significant increase in accuracy performance. The average accuracy increase reaches more than 11%, from 0.802 to 0.919, highlighting its potential for broader applicability and enhanced performance