Results 161 to 170 of about 208,530 (213)

INCREMENTAL LEARNING DECISION TREE ALGORITHM FOR KNOWLEDGE DISCOVERY

PONTE International Scientific Researchs Journal, 2016
A prominent learning discovery procedure is Data Mining. Decision trees are of the basic and intense decision making models in data mining. A single constraint in decision trees is the unpredictability and error rate. Motivated by human learning techniques, we suggest a decision tree structure which impersonates human adapting by performing consistent ...
Mohammed Moulana, Mohammed Ali Hussain
openaire   +1 more source

Mitigating concept drift in data streams: an incremental decision tree approach

Soft Computing
Hadi Tarazodar, Karamollah Bagherifard, Samad Nejatian, Hamïd Parvïn, Razieh Malekhosseini   +4 more
openalex   +2 more sources

STABLE DECISION TREES: USING LOCAL ANARCHY FOR EFFICIENT INCREMENTAL LEARNING

International Journal on Artificial Intelligence Tools, 2000
This work deals with stability in incremental induction of decision trees. Stability problems arise when an induction algorithm must revise a decision tree very often and oscillations between similar concepts decrease learning speed. We introduce a heuristic and an algorithm with theoretical and experimental backing to tackle this problem.
DIMITRIOS KALLES, ATHANASIOS PAPAGELIS
openaire   +1 more source

A New Incremental Learning Technique For Decision Trees With Thresholds

SPIE Proceedings, 1989
This paper presents some basic algorithms for manipulating decision trees with thresholds. The algorithms are based on discrete decision theory. This algebraic approach to discrete decision theory, in particular, provides syntactic techniques for reducing the size of decision trees.
J. Robin, B. Cockett, Yunzhou Zhu
openaire   +1 more source

Incremental Induction of Belief Decision Trees in Averaging Approach

2014
This paper extends the belief decision tree learning method to an incremental mode where the tree structure could change when new data come. This so-called incremental belief decision tree is a new classification method able to learn new instances incrementally, by updating and restructuring an existing belief decision tree.
Salsabil Trabelsi, Zied Elouedi, Mouna El Aroui   +2 more
openaire   +1 more source

Incrementally Optimized Decision Tree for Mining Imperfect Data Streams

2012
The Very Fast Decision Tree (VFDT) is one of the most important classification algorithms for real-time data stream mining. However, imperfections in data streams, such as noise and imbalanced class distribution, do exist in real world applications and they jeopardize the performance of VFDT.
Hang Yang, Simon Fong
openaire   +1 more source

Research on time series data mining algorithm based on Bayesian node incremental decision tree

Cluster Computing, 2017
Xingrong Sun
semanticscholar   +2 more sources

CONSOLIDATING A HEURISTIC FOR INCREMENTAL DECISION TREE LEARNING THROUGH ASYMPTOTIC ANALYSIS

International Journal on Artificial Intelligence Tools, 2011
This paper addresses stability issues in incremental induction of decision trees. Stability problems arise when an induction algorithm must revise a decision tree very often and oscillations between similar concepts decrease learning speed. We review a heuristic that solves this problem and subsequently employ asymptotic analysis to approximate the ...
DIMITRIS KALLES, ATHANASIOS PAPAGELIS, YANNIS C. STAMATIOU   +2 more
openaire   +1 more source

Safety Content Filtering Based on Incremental Induction of Decision Trees

2012 Sixth International Conference on Internet Computing for Science and Engineering, 2012
This paper presents a novel method of safety content filtering exploiting incremental induction of decision trees. Via defining an appropriate attribute space, this approach adapts the security content filtering task to online incremental induction of decision trees learning framework.
openaire   +1 more source

Performance evaluation of incremental decision tree learning under noisy data streams

International Journal of Computer Applications in Technology, 2013
Big data has become a significant problem in software applications nowadays. Extracting classification model from such data requires an incremental learning process. The model should update when new data arrive, without re-scanning historical data. A single-pass algorithm suits continuously arrival data environment. However, one practical and important
Hang Yang, Simon Fong
openaire   +1 more source