Classification Based on Decision Tree Algorithm for Machine Learning

Charbuty, Bahzad; Abdulazeez, Adnan Mohsin

doi:10.38094/jastt20165

Cited by 904 publications

(404 citation statements)

References 69 publications

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“…A decision tree is a tree structure with nodes such as the root, intermediate, and leaf nodes. Both classification and regression are done with a decision tree [27][28][29][30].…”

Section: A-decision Treementioning

confidence: 99%

Plant Leaf Disease Detection by Using Different Classification Techniques: Comparative

Hassan

Abdulazeez

2021

AJRCoS

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One of the main factors that assist to increase the growth of any country is Agriculture. The detection of diseases from plant leaf images is one of the most important fields of agricultural research. To identify disease factors, this field requires a reliable prediction approach. Data Mining (DM) is the process of analyzing data from different aspects and summarizing it into valuable information. It helps users to categorize and identify relationships between data from various dimensions. As there are many plants on the farm, detecting and classifying the diseases of each plant on the farm is extremely difficult for the human eye. And diagnosing each plant is very critical since these diseases may spread. DM classification is an important method that has a wide range of applications. It classifies each item in a set of data into one of a set of predefined classes. In this paper, a comparison of different DM classification methods such as Naive Bayes, Decision trees, SVM, and Random Forest algorithms has been illustrated by using of Weka Tool.

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“…A decision tree is a tree structure with nodes such as the root, intermediate, and leaf nodes. Both classification and regression are done with a decision tree [27][28][29][30].…”

Section: A-decision Treementioning

confidence: 99%

Plant Leaf Disease Detection by Using Different Classification Techniques: Comparative

Hassan

Abdulazeez

2021

AJRCoS

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“…However, there is a negative relationship between the tree dimensions and interpretation ability. In other words, an increase in the S. Lotfi a , M. Ghasemzadeh *b , M. Mohsenzadeh a , M. Mirzarezaee a complexity of the tree results in decreasing the interpretation ability [4]. It is worth noting that the complexity of the tree is measured by some criteria such as the total number of nodes, the tree levels, the depth of the tree, and the number of traits used.…”

Section: Complexity Of Decision Treementioning

confidence: 99%

The Construction of Scalable Decision Tree based on Fast Splitting and J-Max Pre-Pruning on Large Datasets

Lotfi

Ghasemzadeh

Mohsenzadeh

et al. 2021

IJE

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The decision tree is one of the most important algorithms in the classification which offers a comprehensible model of data. In building a tree we may encounter a memory limitation. The present study aims to implement an incremental scalable approach based on fast splitting, and employs a pruning technique to construct the decision tree on a large dataset to reduce the complexity of the tree. The proposed algorithm constructs the decision tree without storing the entire dataset in the primary memory via employing a minimum number of parameters. Furthermore, the J-max Pre pruning method was used to reduce the complexity with acceptable results. Experimental results show that this approach can create a balance between the accuracy and complexity of the tree and overcome the difficulties of the complexity of the tree. In spite of the appropriate accuracy and time, the proposed algorithm could produce a decision tree with less complexity on a large dataset.

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“…Recently, technology has developed a lot, especially in the field of Machine Learning (ML), which is useful for reducing human work. In the field of artificial intelligence, ML integrates statistics and computer science to build algorithms that get more efficient when they are subject to relevant data rather than being given specific instructions [1][2] [3].…”

Section: Introductionmentioning

confidence: 99%

Birds Sound Classification Based on Machine Learning Algorithms

Mehyadin

Abdulazeez

Hasan

et al. 2021

AJRCoS

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The bird classifier is a system that is equipped with an area machine learning technology and uses a machine learning method to store and classify bird calls. Bird species can be known by recording only the sound of the bird, which will make it easier for the system to manage. The system also provides species classification resources to allow automated species detection from observations that can teach a machine how to recognize whether or classify the species. Non-undesirable noises are filtered out of and sorted into data sets, where each sound is run via a noise suppression filter and a separate classification procedure so that the most useful data set can be easily processed. Mel-frequency cepstral coefficient (MFCC) is used and tested through different algorithms, namely Naïve Bayes, J4.8 and Multilayer perceptron (MLP), to classify bird species. J4.8 has the highest accuracy (78.40%) and is the best. Accuracy and elapsed time are (39.4 seconds).

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Classification Based on Decision Tree Algorithm for Machine Learning

Cited by 904 publications

References 69 publications

Plant Leaf Disease Detection by Using Different Classification Techniques: Comparative

Plant Leaf Disease Detection by Using Different Classification Techniques: Comparative

The Construction of Scalable Decision Tree based on Fast Splitting and J-Max Pre-Pruning on Large Datasets

Birds Sound Classification Based on Machine Learning Algorithms

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