Combining Classifier Based on Decision Tree

Yu, Ye; Zhang, Fu; Zhao, Xianghui; Cheng, Wenfang

doi:10.1109/icie.2009.12

Cited by 15 publications

(9 citation statements)

References 5 publications

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“…It is a Supervised Machine Learning [17,18] algorithm which continuously divides data based on some parameters or attributes. The main components of a decision tree are stated below:…”

Section: Decision Tree Classifiermentioning

confidence: 99%

A Study on Argument-Based Analysis of Legal Model

Sil

Saha

Roy

2021

Advances in Intelligent Systems and Computing

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Unbounded delay in the delivery of justice has resulted in large number of pending cases thereby affecting the well-being of beneficiary in a broader sense. Due to this delay, complainant, accused or witness may either become unfit for trail or hostile, etc. which may jeopardize the promptness of judicial system. However, its efficiency can be enhanced using Machine Learning algorithm thereby reducing the workload of legal professional so that they can engage more time to resolve those pending cases. This paper focuses on argument-based prediction system over legal data set. Precisely, we have applied Supervised Machine Learning technique over the cases related to 'Domestic Violence Against Women' and have proposed a model to predict guilt of an accused. We have approached in sequential manner, precisely: (i) Hard-copies of argument based legal documents are collected from various trial courts of West Bengal. (ii) Data set are generated manually based on specific parameters which act as deciding factors for prediction of accusation. (iii) Supervised Machine Learning algorithm are used to train those legal data set to predict our desired output. We have validated the performance and accuracy of our proposed legal prediction system using standard classifiers like Decision Tree, Naive-Bayes Classifier and K-Nearest Neighbor (KNN).

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“…It is a Supervised Machine Learning [17,18] algorithm which continuously divides data based on some parameters or attributes. The main components of a decision tree are stated below:…”

Section: Decision Tree Classifiermentioning

confidence: 99%

A Study on Argument-Based Analysis of Legal Model

Sil

Saha

Roy

2021

Advances in Intelligent Systems and Computing

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“…Tek bir algoritmaya göre daha iyi sonuç vermektedir. TÖ, en iyi sınıflandırma yöntemlerinden biridir [17]. Ayrıca genelleme yetenekleri oldukça güçlüdür.…”

Section: Topluluk öğRenmeunclassified

Saldırı Tespiti için Makine Öğrenme Yöntemlerinin Karşılaştırmalı Analizi

Burukanli

Çıbuk

Budak

2021

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

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Özİnternet ve ağ teknolojilerindeki hızlı gelişmeler, siber saldırılar ve izinsiz erişimlerin başta olduğu birçok dezavantajı beraberinde getirmektedir. Bu girişimlerin önceden tespiti, olası saldırıların gerçekleşmeden önlenebilmesini sağlamaktadır. Bu çalışmada makine öğrenme yaklaşımlarının saldırı tespiti üzerindeki performansları araştırılmıştır. Tüm deneyler, açık erişime sunulmuş ve yaygın olarak kullanılan KDD'99 veri kümesi altındaki KDD10CORRECTED ve KDDTEST setleri üzerinde gerçekleştirilmiştir. Sınıflandırıcı olarak, KA, TÖ ve DVM tercih edilmiştir. Veri setleri hem doğrudan sınıflandırıcıların girişi olarak hem de boyut indirgeme tekniği olan TBA uygulanarak sınıflandırılmıştır. Sınıflandırma aşamasında 5-kat çapraz doğrulama tekniği kullanılmıştır. En iyi başarım oranları KDD10CORRECTED veri setinde üzerinde %99,99 ile Torbalama sınıflandırıcısı, KDDTEST veri setinde üzerinde %97,90 ile Torbalama sınıflandırıcısı, KDD10CORRECTED+KDDTEST veri setinde üzerinde %100 ile Torbalama sınıflandırıcısı elde etmiştir. Elde edilen sonuçlar kıyaslanarak rapor edilmiştir. Sonuçlar gelecekteki çalışmalar için cesaret vericidir.Anahtar kelimeler: Saldırı tespiti, topluluk öğrenme, destek vektör makinesi, karar ağacı, temel bileşen analizi.

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“…First, each traveler is assigned a unique contextual preference model built using their data. For this purpose, THOR uses various well-known classification algorithms-i.e., K-Nearest Neighbors (KNN) [19], Support Vector Classifier (SVC) [20], Decision Tree (DT) [21], Random Forest (RF) [22], and Logistic Regression (LR) [23]-and finds the best set of hyper-parameters. Then, when the system receives a set of travel offers to be shown to the user, it exploits the contextual preference model to determine, for each offer, the probability that the user will buy it and ranks the offers accordingly.…”

Section: Introductionmentioning

confidence: 99%

THOR: A Hybrid Recommender System for the Personalized Travel Experience

Sabet

Shekari

Guan

et al. 2022

BDCC

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One of the travelers’ main challenges is that they have to spend a great effort to find and choose the most desired travel offer(s) among a vast list of non-categorized and non-personalized items. Recommendation systems provide an effective way to solve the problem of information overload. In this work, we design and implement “The Hybrid Offer Ranker” (THOR), a hybrid, personalized recommender system for the transportation domain. THOR assigns every traveler a unique contextual preference model built using solely their personal data, which makes the model sensitive to the user’s choices. This model is used to rank travel offers presented to each user according to their personal preferences. We reduce the recommendation problem to one of binary classification that predicts the probability with which the traveler will buy each available travel offer. Travel offers are ranked according to the computed probabilities, hence to the user’s personal preference model. Moreover, to tackle the cold start problem for new users, we apply clustering algorithms to identify groups of travelers with similar profiles and build a preference model for each group. To test the system’s performance, we generate a dataset according to some carefully designed rules. The results of the experiments show that the THOR tool is capable of learning the contextual preferences of each traveler and ranks offers starting from those that have the higher probability of being selected.

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Combining Classifier Based on Decision Tree

Cited by 15 publications

References 5 publications

A Study on Argument-Based Analysis of Legal Model

A Study on Argument-Based Analysis of Legal Model

Saldırı Tespiti için Makine Öğrenme Yöntemlerinin Karşılaştırmalı Analizi

THOR: A Hybrid Recommender System for the Personalized Travel Experience

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