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Stratified B-trees and versioning dictionaries [article]

Andy Twigg, Andrew Byde, Grzegorz Milos, Tim Moreton, John Wilkes, Tom Wilkie
2011 arXiv   pre-print
We describe the 'stratified B-tree', which beats all known semi-external memory versioned B-trees, including the CoW B-tree.  ...  A classic versioned data structure in storage and computer science is the copy-on-write (CoW) B-tree -- it underlies many of today's file systems and databases, including WAFL, ZFS, Btrfs and more.  ...  inserted we call 'stratified B-trees'.  ... 
arXiv:1103.4282v2 fatcat:vf6yaxk5znhdncrwzg2uz2ecvy

Another Proof of Oscar Rojo's Theorems [article]

Hao Chen, Jürgen Jost
2010 arXiv   pre-print
We present here another proof of Oscar Rojo's theorems about the spectrum of graph Laplacian on certain balanced trees, by taking advantage of the symmetry properties of the trees in question, and looking  ...  Introduction Oscar Rojo has proved, first for balanced binary trees [1] , then extended to balanced trees such that vertices at the same level l are of the same degree d(l) [2] , that Theorem 1.  ...  Figure 3 : 3 An example of stratified structure on the whole tree.  ... 
arXiv:1011.3361v1 fatcat:yye67k273jcujfvzitix4y4tmi

On the combination of evolutionary algorithms and stratified strategies for training set selection in data mining

José Ramón Cano, Francisco Herrera, Manuel Lozano
2006 Applied Soft Computing  
The analysis follows two evaluating approaches: balance between reduction and accuracy of the subsets selected, and balance between interpretability and accuracy of the representation models associated  ...  The performance of the proposal is compared with seven non-evolutionary algorithms, in stratified execution.  ...  Our proposal significantly reduces the size of the decision tree associated to the model obtained. This characteristic produces decision trees that are easier to interpret.  ... 
doi:10.1016/j.asoc.2005.02.006 fatcat:z4rusv5bufd3jobqrhmhan5squ

Page 4263 of Mathematical Reviews Vol. , Issue 86i [page]

1986 Mathematical Reviews  
We present algorithms for internal- and external- search trees in the general framework of stratified trees.  ...  This enables us to demonstrate that many classes of balanced search trees have such updating schemes, although, for example, weight- balanced trees do not fit into this framework.” 86i:68017 Tamminen,  ... 

Pattern Classification with Imbalanced and Multiclass Data for the Prediction of Albendazole Adverse Event Outcomes

Pınar Yıldırım
2016 Procedia Computer Science  
When given an unknown sample, a k-nearest neighbour classifier searches the pattern space for the k training samples that are closest to the unknown sample.  ...  Stratified Removed Fold: Generates output a specified stratified cross-validation fold for the dataset 8 .  ... 
doi:10.1016/j.procs.2016.04.216 fatcat:ptqt5jd7nrbh3j7r3v353xurte

Two- and three- dimensional point location in rectangular subdivisions [chapter]

Mark Berg, Marc Kreveld, Jack Snoeyink
1992 Lecture Notes in Computer Science  
Like other results on stratified trees, our algorithms run on a RAM model and make use of perfect hashing.  ...  We apply van Emde Boas-type stratified trees to point location problems in rectangular subdivisions in 2 and 3 dimensions.  ...  We next form a level-search tree, a balanced k-ary search tree on the levels of T. Figure 2 shows an interval tree with a ternary level-search tree.  ... 
doi:10.1007/3-540-55706-7_32 fatcat:4t2psyhl4fatrobscczx56cqy4

Stratified Sampling for Even Workload Partitioning Applied to IDA* and Delaunay Algorithms

Jeeva Paudel, Levi H. S. Lelis, Jose Nelson Amaral
2015 2015 IEEE International Parallel and Distributed Processing Symposium  
In WPS, a stratified sampling technique estimates the number of work items that will be processed in each step of the target application.  ...  A coordination between WPS and existing work-stealing schedulers for intranode load balancing yields additional speedups in the range of 18% to 40% compared to that achieved with the existing workstealing  ...  Knuth observed that his technique was not effective when the tree is imbalanced. Chen [4] addressed this problem by stratifying the search tree to reduce the variance of the sampling process.  ... 
doi:10.1109/ipdps.2015.63 dblp:conf/ipps/PaudelLA15 fatcat:hlnzaa2fr5arddok6acz6z3t3m

Windowing as a Sub-Sampling Method for Distributed Data Mining

David Martínez-Galicia, Alejandro Guerra-Hernández, Nicandro Cruz-Ramírez, Xavier Limón, Francisco Grimaldo
2020 Mathematical and Computational Applications  
and the similitude metric Sim1; and compared to those obtained when using traditional methods: random, balanced, and stratified samplings.  ...  Windowing is a sub-sampling method, originally proposed to cope with large datasets when inducing decision trees with the ID3 and C4.5 algorithms.  ...  For example, less complex decision trees, as those induced by random, balanced and stratified samplings, are more general but less accurate.  ... 
doi:10.3390/mca25030039 fatcat:pjle3dynzjdohahtamilijb42q

New trie data structures which support very fast search operations

Dan E. Willard
1984 Journal of computer and system sciences (Print)  
nonnegative integers less than some initially specified bound M, a q-fast trie uses space O(N) and time O(m) for insertions, deletions, and all the retrieval operations commonly associated with binary trees  ...  stratified trees.  ...  The active internal nodes play a major role in the retrieval algorithm of [ 171; each active internal node may be visited during the search of a stratified tree.  ... 
doi:10.1016/0022-0000(84)90020-5 fatcat:reni3u7cqfhupdyuqooljv3hgm

TOP DOWNLOADED PAPERS-Artificial Intelligence & Applications (IJAIA)

J.S.Saleema, N.Bhagawathi
2019 Zenodo  
At the next level the balanced stratified sampling with variations as per the choice of the prognosis class labels have been tested.  ...  The results shows a steady increase in the prediction accuracy of balanced stratified model as the sample size increases, but the traditional approach fluctuates before the optimum results.  ...  The referred data structure is habitually a B-tree, however, can be a hash table or some other logic structure as well.  ... 
doi:10.5281/zenodo.2556472 fatcat:dz2xk4k5qfdqflxic2rfle636i

HASPO: Harmony Search-Based Parameter Optimization for Just-in-Time Software Defect Prediction in Maritime Software

Jonggu Kang, Sunjae Kwon, Duksan Ryu, Jongmoon Baik
2021 Applied Sciences  
Harmony search (HS) is a widely used music-inspired meta-heuristic optimization algorithm.  ...  Experiments with open source software also showed better recall for all datasets despite the fact that we considered balance as a performance index.  ...  • We first apply HS-based parameter optimization with search space of the decision tree and bagging classifier together with balanced fitness function in JIT-SDP.  ... 
doi:10.3390/app11052002 fatcat:4z3qo7gtgrantlg2l7o3kz5rn4

Memory-Efficient Tree Size Prediction for Depth-First Search in Graphical Models [chapter]

Levi H. S. Lelis, Lars Otten, Rina Dechter
2014 Lecture Notes in Computer Science  
We address the problem of predicting the size of the search tree explored by Depth-First Branch and Bound (DFBnB) while solving optimization problems over graphical models.  ...  called Two-Step Stratified Sampling (TSS).  ...  Or, in the context of parallelizing search, a prediction scheme could facilitate load-balancing by partitioning the problem into subproblems of similar EST sizes [6] .  ... 
doi:10.1007/978-3-319-10428-7_36 fatcat:xl2ocmdjrve4lgjofnqi3wcmfi

Using evolutionary algorithms as instance selection for data reduction in KDD: an experimental study

J.R. Cano, F. Herrera, M. Lozano
2003 IEEE Transactions on Evolutionary Computation  
Evolutionary algorithms are adaptive methods based on natural evolution that may be used for search and optimization.  ...  As data reduction in knowledge discovery in databases (KDDs) can be viewed as a search problem, it could be solved using evolutionary algorithms (EAs).  ...  CHC with the stratified strategy is the best algorithm evaluated for large size data sets. It offers the best balance between reduction and accuracy rates.  ... 
doi:10.1109/tevc.2003.819265 fatcat:pdhwsvppfnevfph5drehnxckpu

Effective predictive modelling for coronary artery diseases using support vector machine

Kuncahyo Setyo Nugroho, Anantha Yullian Sukmadewa, Angga Vidianto, Wayan Firdaus Mahmudy
2022 IAES International Journal of Artificial Intelligence (IJ-AI)  
Furthermore, the grid search on SVM cross-validated ten times had more accurate training model results and achieved 88% accuracy on the test data.  ...  Stratified k-fold schema Figure 3 . 3 Figure3.  ...  One type of this procedure is a Stratified K-fold, as shown in Figure 2 . Stratified K-Fold is helpful if the available dataset is few and has an unbalanced class distribution.  ... 
doi:10.11591/ijai.v11.i1.pp345-355 fatcat:ukjo4uksbjg3ddo72lsfqcsnme

TPOT: A Tree-based Pipeline Optimization Tool for Automating Machine Learning

Randal S. Olson, Jason H. Moore
2016 International Conference on Machine Learning  
The TPOT GP algorithm follows a standard GP process: To begin, the GP algorithm generates 100 random tree-based pipelines and evaluates their balanced cross-validation accuracy on the data set.  ...  Figure 2 : 2 Figure 2: Box plots showing the distribution of balanced accuracies for the 25 benchmarks with a significant difference in median accuracy between TPOT and a Random Forest with 500 trees.  ... 
dblp:conf/icml/OlsonM16 fatcat:lvw43gpprneqxbnrbeiigjsely
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