Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm [PDF]
Journal of Artificial Intelligence Research, Vol 2, (1995),
369-409, 1995 This paper introduces ICET, a new algorithm for cost-sensitive classification. ICET uses a genetic algorithm to evolve a population of biases for a decision tree induction algorithm. The fitness function of the genetic algorithm is the average cost of classification when using the decision tree, including both the costs of tests (features, measurements)
arxiv
Study of The ID3 and C4.5 Learning Algorithms [PDF]
, 2020 Youssef Fakir +2 more
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The Concept Design of Rice Quality Detection System Using Model-Based System Engineering Approach
Journal of Applied Agricultural Science and TechnologyQuality of rice is determined by several factors such as water content, broken grains, and whiteness. The approach often used for the measurement is manual, time-consuming, and prone to error.
Purwa Tri Cahyana +4 more
doaj +1 more source
Impact of Photovoltaic Systems Placement, Sizing on Power Quality in Distribution Network
Advances in Electrical and Computer Engineering, 2018 The paper presents a Decision Tree Algorithm for impact of photovoltaic systems placement and sizing in order to improve power quality in distribution networks. The proposed approach is based primarily on ID3 and J48 Decision Tree Algorithms.
YMERI, A., MUJOVIC, S.
doaj +1 more source
Unifying Decision Trees Split Criteria Using Tsallis Entropy [PDF]
arXiv, 2015 The construction of efficient and effective decision trees remains a key topic in machine learning because of their simplicity and flexibility. A lot of heuristic algorithms have been proposed to construct near-optimal decision trees. ID3, C4.5 and CART are classical decision tree algorithms and the split criteria they used are Shannon entropy, Gain ...
arxiv
Top-down particle filtering for Bayesian decision trees [PDF]
JMLR W&CP 28(3):280-288, 2013, 2013 Decision tree learning is a popular approach for classification and regression in machine learning and statistics, and Bayesian formulations---which introduce a prior distribution over decision trees, and formulate learning as posterior inference given data---have been shown to produce competitive performance.
arxiv
Modeling Suspicious Email Detection using Enhanced Feature Selection [PDF]
IJMO 2012 Vol.2(4): 371-377 ISSN: 2010-3697, 2013 The paper presents a suspicious email detection model which incorporates enhanced feature selection. In the paper we proposed the use of feature selection strategies along with classification technique for terrorists email detection. The presented model focuses on the evaluation of machine learning algorithms such as decision tree (ID3), logistic ...
arxiv
An extended ID3 decision tree algorithm for spatial data [PDF]
, 2011 Imas Sukaesih Sitanggang +3 more
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The Application of Data Mining to Build Classification Model for Predicting Graduate Employment [PDF]
arXiv, 2013 Data mining has been applied in various areas because of its ability to rapidly analyze vast amounts of data. This study is to build the Graduates Employment Model using classification task in data mining, and to compare several of data-mining approaches such as Bayesian method and the Tree method.
arxiv
Detection of Speech Delayed in Children using Iterative Dichotomiser 3 (Id3) Algorithm: Prematurity, Gender, Family History of Speech Delay, Education, and Occupation [PDF]
, 2021 Nita Hestiyana +2 more
openalex +1 more source