Study on Decision Tree and KNN Algorithm for Intrusion Detection System

Pathak, Ashwini; Pathak, Sakshi; Sgsits, Indore

doi:10.17577/ijertv9is050303

Cited by 16 publications

(4 citation statements)

References 10 publications

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“…One common model for data categorization is the decision tree, which resembles a tree. Dividing data into branches, which may be other trees or even just plain old nodes, is what a decision tree does [15]. There are three possible types of nodes in a decision tree.…”

Section: B Decision Treementioning

confidence: 99%

Investigating Role of Supervised Machine Learning Approach in Classification of Diabetic Patient

Sarita Kumari

2024

jes

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Objectives: Healthcare analytics requires classifying diabetic patient datasets for quicker diagnosis and personalized treatment. This study used SVM, Decision Trees, KNN, ANN, and Logistic Regression to predict type 1 and 2 diabetes. Our detailed performance research shows these algorithms' utility in handling diabetic patient data's complexity. Methods: We compare SVM, Decision Trees, KNN, ANN, and Logistic Regression for diabetes patient dataset classification. While each approach has pros and cons, ANN and logistic regression are promising clinical possibilities. Diagnoses, proactive therapies, and diabetes patient outcomes increase with these breakthroughs. Results: SVM has 84.3% accuracy in type 1 and type 2 diabetics. SVM recognized complicated dataset patterns with great accuracy and recall. Decision trees were more interpretable and could record diverse choice limits, with accuracy rates of 86.15% for both types. With 90.6% accuracy, KNN predicted type 1 diabetes well. KNN was ideal for complex datasets because to its greater accuracy and recall using data point similarities. ANN and Logistic Regression had the highest accuracy for type 1 and type 2 diabetes patients at 96.1% and 97%, respectively. Novelty: Layered ANN and logistic regression identified complicated dataset relationships with accuracy, recall, and F1 scores exceeding 0.94. ANN with logistic regression may change diabetes patient classification with unequaled prediction power and accuracy.

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Section: B Decision Treementioning

confidence: 99%

Investigating Role of Supervised Machine Learning Approach in Classification of Diabetic Patient

Sarita Kumari

2024

jes

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“…Prosesnya dilakukan secara rekursif dari root hingga leaf node untuk memecah proses pengambilan keputusan menjadi lebih sederhana [7]. Studi sebelumnya telah menggunakan DT untuk sistem deteksi intrusi dengan tingkat akurasi mencapai 99,15% dan menggunakan data KDDCUP'99 sebagai datasetnya [8]. Dataset KDDCUP'99 memberikan pemahaman terbaik tentang berbagai serangan intrusi adalah dataset KDD.…”

Section: Pendahuluanunclassified

Perbandingan Kinerja Algoritma K-Nearest Neighbors (K-NN) Dan Decision Tree dalam Deteksi Paket Malis pada Jaringan

Kasmara,

Handayani,

Nathasia

2024

STRING

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Penelitian ini bertujuan untuk melakukan klasifikasi terhadap data paket malis dan membandingkan performa dua algoritma, yaitu K-Nearest Neighbor (K-NN) dan Decision Tree (DT). Dataset UNSW-NB15 yang digunakan untuk penelitian ini telah melalui tahap preprocessing, feature selection, dan data split. Tahap preprocessing termasuk transformasi data dan pemilihan fitur yang relevan untuk mendeteksi paket malis. Selanjutnya, eksperimen dilakukan untuk menguji variasi nilai K pada K-NN dan mengukur akurasi, recall, precision, dan F1-Score. Hasilnya menunjukkan bahwa K-NN memiliki akurasi 91.54%, sedangkan DT memiliki 92.41%. Kesimpulan dari penelitian ini menunjukkan bahwa algoritma Decision Tree (DT) memiliki kinerja yang sedikit lebih baik daripada K-Nearest Neighbor (K-NN) dalam mendeteksi paket malis. Oleh karena itu, dalam memilih algoritma untuk deteksi keamanan jaringan, penting untuk mempertimbangkan kebutuhan dan tujuan spesifik penelitian serta karakteristik data yang digunakan.

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“…Decision Tree Classifiers are a popular supervised learning method used in machine learning for both classification and regression tasks [34]. Their strength lies in their intuitive, tree-like structure that breaks down complex decisions into a series of simpler ones, mirroring human-like reasoning.…”

Section: E K-nearest Neighbor (Knn) Classifiermentioning

confidence: 99%

Comparative Evaluation Of Machine Learning Classifiers For Brain Tumor Detection

Ali

2024

Preprint

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Brain tumors, which are abnormal growths of cells in the brain, represent a significant health concern, necessitating prompt and accurate detection for effective treatment. If left untreated, brain tumors can lead to severe complications, including cognitive impairment, paralysis, and even death. This study evaluates six machine learning classifiers: Support Vector Classifier (SVC), Logistic Regression Classifier, K-Nearest Neighbors (KNN) Classifier, Naive Bayes Classifier, Decision Tree Classifier, and Random Forest Classifier - on a comprehensive brain tumor dataset. Our results showed that Random Forest achieved the highest accuracy of 98.27%, demonstrating its potential in detecting brain tumors. However, Support Vector Classifier (SVC) emerged as the top performer, achieving an impressive accuracy of 97.74%, showcasing its exceptional ability to detect brain tumors accurately. This significant improvement in SVC’s performance highlights its potential as a reliable tool for medical diagnostics, contributing to the development of efficient and accurate automated systems for early brain tumor diagnosis, ultimately aiming to improve patient outcomes and treatment efficacy.

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Study on Decision Tree and KNN Algorithm for Intrusion Detection System

Cited by 16 publications

References 10 publications

Investigating Role of Supervised Machine Learning Approach in Classification of Diabetic Patient

Investigating Role of Supervised Machine Learning Approach in Classification of Diabetic Patient

Perbandingan Kinerja Algoritma K-Nearest Neighbors (K-NN) Dan Decision Tree dalam Deteksi Paket Malis pada Jaringan

Comparative Evaluation Of Machine Learning Classifiers For Brain Tumor Detection

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