Marija Bacauskiene

TL;DR: Random forests has become a popular technique for classification, prediction, studying variable importance, variable selection, and outlier detection, and results of new tests regarding variable rankings based on RF variable importance measures are presented.

TL;DR: The algorithm developed outperformed the other methods by achieving higher classification accuracy on all the problems tested and compared the approach with five other feature selection methods, each of which banks on a different concept.

TL;DR: This paper presents four schemes for soft fusion of the outputs of multiple classifiers using Zimmermann's compensatory operator, and an empirical evaluation substantiates the validity of the approaches with data-dependent weights, compared to various existing combination schemes of multipleclassifiers.

TL;DR: This paper presents a comprehensive review of hybrid and ensemble-based soft computing techniques applied to bankruptcy prediction, namely how different techniques are combined, but not on obtained results.

TL;DR: The effectiveness of 11 different feature sets in classification of voice recordings of the sustained phonation of the vowel sound /a/ into a healthy and two pathological classes, diffuse and nodular, is investigated.