A Fuzzy Rule Based Approach to Geographic Classification of Virgin Olive Oil Using T-Operators

Olive oil is an important agricultural food product. Especially, protected designation of origin (PDO) and protected geographic indications (PGI) are useful to protect the intellectual property rights of the consumers and producers. For this reason, the importance of the geographic classification increases to trace geographical indications. This chapter suggests a geographical classification system for the virgin olive oils. This system is formed on chemical parameters. These parameters include fuzziness. Novel proposed system constructs the rules by using fuzzy decision tree algorithm. It produces rules over fuzzy ID3 algorithm. It uses fuzzy entropy on the fuzzified data. The reasoning procedure depends on weighted rule-based system and is adapted into the fuzzy reasoning handled with different T-operators. Fuzzification is performed with fuzzy c-means algorithm for the olive oil data set. The cluster numbers of each variable are selected based on partition coefficient validity criteria. The model is examined by using different decision tree approaches (C4.5 and standard version fuzzy ID3 algorithm) and FID3 reasoning method with eight different T-operators. Also, the conclusions are supported by statistical analysis. Experimental results support that the weights have important manner on fuzzy reasoning method for the geographic classification system

Analysis of Data Mining Classification with Decision Tree Technique

The diversity and applicability of data mining are increasing day to day so need to extract hidden patterns from massive data. The paper states the problem of attribute bias. Decision tree technique based on information of attribute is biased toward multi value attributes which have more but insignificant information content. Attributes that have additional values can be less important for various applications of decision tree. Problem affects the accuracy of ID3 Classifier and generate unclassified region. The performance of ID3 classification and cascaded model of RBF network for ID3 classification is presented here. The performance of hybrid technique ID3 with CRBF for classification is proposed. As shown through the experimental results ID3 classifier with CRBF accuracy is higher than ID3 classifier

A Forest of Possibilities: Decision Trees and Beyond

Decision trees are fundamental in machine learning due to their interpretability and versatility. They are hierarchical structures used for classification and regression tasks, making decisions by recursively splitting data based on features. This abstract explores decision tree algorithms, tree construction, pruning to prevent overfitting, and ensemble methods like Random Forests. Additionally, it covers handling categorical data, imbalanced datasets, missing values, and hyperparameter tuning. Decision trees are valuable for feature selection and model interpretability. However, they have drawbacks, such as overfitting and sensitivity to data variations. Nevertheless, they find applications in fields like finance, medicine, and natural language processing, making them a critical topic in machine learning

Attribute related methods for improvement of ID3 Algorithm in classification of data: A review

Decision tree is an important method in data mining to solve the classification problems. There are several learning algorithms to implement the decision tree but the most commonly-used is ID3 algorithm. Nevertheless, there are some limitations in ID3 algorithm that can affect the performance in the classification of data. The use of information gain in the ID3 algorithm as the attribute selection criteria is not to assess the relationship between classification and the dataset’s attributes. The objective of the study being conducted is to implement the attribute related methods to solve the shortcomings of the ID3 algorithm like the tendency to select attributes with many values and also improve the performance of ID3 algorithm. The techniques of attribute related methods studied in this paper were mutual information, association function and attribute weighted. All the techniques assist the decision tree to find the most optimal attributes in each generation of the tree. Results of the reviewed techniques show that attribute selection methods capable to resolve the limitations in ID3 algorithm and increase the performance of the method. All of the reviewed techniques have their advantages and disadvantages and useful to solve the classification problems. Implementation of the techniques with ID3 algorithm is being discussed thoroughly