Results 1 to 10 of about 524,166 (313)

Suite of decision tree-based classification algorithms on cancer gene expression data

Egyptian Informatics Journal, 2011
One of the major challenges in microarray analysis, especially in cancer gene expression profiles, is to determine genes or groups of genes that are highly expressed in cancer cells but not in normal cells. Supervised machine learning techniques are used
Mohmad Badr Al Snousy, Hesham Mohamed El-Deeb, Khaled Badran, Ibrahim Ali Al Khlil   +3 more
doaj   +1 more source

Optimizing Interpretable Decision Tree Policies for Reinforcement Learning [PDF]

arXiv
Reinforcement learning techniques leveraging deep learning have made tremendous progress in recent years. However, the complexity of neural networks prevents practitioners from understanding their behavior. Decision trees have gained increased attention in supervised learning for their inherent interpretability, enabling modelers to understand the ...
arxiv  

Efficient non-greedy optimization of decision trees [PDF]

arXiv, 2015
Decision trees and randomized forests are widely used in computer vision and machine learning. Standard algorithms for decision tree induction optimize the split functions one node at a time according to some splitting criteria. This greedy procedure often leads to suboptimal trees.
arxiv  

Speeding Up Relational Reinforcement Learning through the Use of an Incremental First Order Decision Tree Learner [PDF]

, 2001
Kurt Driessens, Jan Ramon, Hendrik Blockeel   +2 more
openalex   +1 more source

Provably optimal decision trees with arbitrary splitting rules in polynomial time [PDF]

arXiv
In this paper, we introduce a generic data structure called decision trees, which integrates several well-known data structures, including binary search trees, K-D trees, binary space partition trees, and decision tree models from machine learning. We provide the first axiomatic definition of decision trees.
arxiv  

Decision tree learning on very large data sets [PDF]

, 2002
Lawrence Hall, Nitesh V. Chawla, Kevin W. Bowyer   +2 more
openalex   +1 more source

Decision Tree Design for Classification in Crowdsourcing Systems [PDF]

arXiv, 2018
In this paper, we present a novel sequential paradigm for classification in crowdsourcing systems. Considering that workers are unreliable and they perform the tests with errors, we study the construction of decision trees so as to minimize the probability of mis-classification. By exploiting the connection between probability of mis-classification and
arxiv  

Binary Halftone Image Resolution Increasing by Decision Tree Learning [PDF]

, 2004
Hae Yong Kim
openalex   +1 more source

Benchmarking Assessment of Supervised Machine Learning Algorithms of K-Nearest Neighbor, Random Forest, Decision Tree and Its Variants Based On Efficiency and Performance Metrics

Journal of Applied Sciences and Environmental Management
Machine learning provides more verbose algorithms capable of accurately predicting, classifying groups as needed. Consequently, the objective of this paper is to assess the benchmarking of Supervised Machine Learning Algorithms of K-Nearest Neighbor ...
Y. Y. Abdullahi, A. S. Nur, A. Sale
doaj  

Causal Discovery and Classification Using Lempel-Ziv Complexity [PDF]

arXiv
Inferring causal relationships in the decision-making processes of machine learning algorithms is a crucial step toward achieving explainable Artificial Intelligence (AI). In this research, we introduce a novel causality measure and a distance metric derived from Lempel-Ziv (LZ) complexity.
arxiv