Design an Optimal Decision Tree based Algorithm to Improve Model Prediction Performance

Performance of decision trees is assessed by prediction accuracy for unobserved occurrences. In order to generate optimised decision trees with high classification accuracy and smaller decision trees, this study will pre-process the data. In this study, some decision tree components are addressed and enhanced. The algorithms should produce precise and ideal decision trees in order to increase prediction performance. Additionally, it hopes to create a decision tree algorithm with a tiny global footprint and excellent forecast accuracy. The typical decision tree-based technique was created for classification purposes and is used with various kinds of uncertain information. Prior to preparing the dataset for classification, the uncertain dataset was first processed through missing data treatment and other uncertainty handling procedures to produce the balanced dataset. Three different real-time datasets, including the Titanic dataset, the PIMA Indian Diabetes dataset, and datasets relating to heart disease, have been used to test the proposed algorithm. The suggested algorithm's performance has been assessed in terms of the precision, recall, f-measure, and accuracy metrics. The outcomes of suggested decision tree and the standard decision tree have been contrasted. On all three datasets, it was found that the decision tree with Gini impurity optimization performed remarkably well

Faculty of Sciences

A comprehensive study of fuzzy rough sets and their application in data reductio

Review and prioritization of investment projects in the Waste Management organization of Tabriz Municipality with a Rough Sets Theory approach

Purpose: Prioritization of investment projects is a key step in the process of planning the investment activities of organizations. Choosing the suitable projects has a direct impact on the profitability and other strategic goals of organizations. Factors affecting the prioritization of investment projects are complex and the use of traditional methods alone cannot be useful, so there is a need to use a suitable model for prioritizing projects and investment plans. The purpose of this study is to prioritize projects and investment methods for projects (10 projects) considered by the Waste Management Organization of Tabriz Municipality. Methodology: The method of analysis used is the theory of rough, so that first the important investment projects in the field of waste management were determined using the research background and opinion of experts and the weight and priority of the projects were obtained using the Rough Sets Theory. Then, the priority of appropriate investment methods (out of 6 methods) of each project was obtained using Rough numbers, the opinion of experts and other aspects. Findings: The result of the research has been that construction project of a specialized recycling town, plastic recycling project, and recycled tire recycling project are three priority projects of Tabriz Municipality Waste Management Organization, respectively. Three investment methods, civil partnership agreements, BOT, and BOO can be used for them. Originality/Value: Tabriz Municipality Waste Management is an important and influential organization in the activities of the city, in which the investment methods in its projects are mostly based on common contracts and are performed in the same way for all projects. This research offers new methods for projects and their diversity according to Rough Sets technique

A Novel Method of the Generalized Interval-Valued Fuzzy Rough Approximation Operators

Rough set theory is a suitable tool for dealing with the imprecision, uncertainty, incompleteness, and vagueness of knowledge. In this paper, new lower and upper approximation operators for generalized fuzzy rough sets are constructed, and their definitions are expanded to the interval-valued environment. Furthermore, the properties of this type of rough sets are analyzed. These operators are shown to be equivalent to the generalized interval fuzzy rough approximation operators introduced by Dubois, which are determined by any interval-valued fuzzy binary relation expressed in a generalized approximation space. Main properties of these operators are discussed under different interval-valued fuzzy binary relations, and the illustrative examples are given to demonstrate the main features of the proposed operators

A note on belief structures and s-approximation spaces

We study relations between evidence theory and S-approximation spaces. Both theories have their roots in the analysis of Dempsterchr('39')s multivalued mappings and lower and upper probabilities, and have close relations to rough sets. We show that an S-approximation space, satisfying a monotonicity condition, can induce a natural belief structure which is a fundamental block in evidence theory. We also demonstrate that one can induce a natural belief structure on one set, given a belief structure on another set, if the two sets are related by a partial monotone S-approximation space

A note on belief structures and s-approximation spaces

We study relations between evidence theory and S-approximation spaces. Both theories have their roots in the analysis of Dempsterchr('39')s multivalued mappings and lower and upper probabilities, and have close relations to rough sets. We show that an S-approximation space, satisfying a monotonicity condition, can induce a natural belief structure which is a fundamental block in evidence theory. We also demonstrate that one can induce a natural belief structure on one set, given a belief structure on another set, if the two sets are related by a partial monotone S-approximation space

Measurement and Fuzzy Scales

Concept measurement presents several difficulties and the tools used to collect qualitative ordinal variables are not always satisfactory. The Likert scale is examined in the context of student evaluation of teaching activity and the measurement approach considers the possibility to apply the fuzzy inference system method to obtain individual values being near the reality and coherent with the prescriptions of the measurement process. The paper presents the results of a survey carried out in the Faculty of Economics at the University of Modena and Reggio Emilia to ascertain the differences between two scales (options): one proposed by the Italian Committee for University System Evaluation (Comitato nazionale per la valutazione del sistema universitario) and another one corresponding to the traditional marks used in the evaluation of student performances in the schools attended before the university (mark scale). The results showed that the latter seemed more coherent with the score (in the decimal scale) assigned to the modalities of the scale