PROBLEM OF A DISCRETE DATA ARRAY APPROXIMATION BY A SET OF ELEMENTARY GEOMETRIC ALGORITHMS

Context. In this paper, a problem of a discrete data array approximation by a set of elementary geometric algorithms and a recognition model representation in a form of algorithmic classification tree has been solved. The object of the present study is a concept of a classification tree in a form of an algorithm trees. The subject of this study are the relevant models, methods, algorithms and schemes of different classification tree construction. Objective. The goal of this work is to create a simple and efficient method and algorithmic scheme of building the tree-like recognition and classification models on the basis of the algorithm trees for training selections of large-volume discrete information characterized by a modular structure of independent recognition algorithms assessed in accordance with the initial training selection data for a wide class of applied tasks. Method. A scheme of classification tree (algorithm tree) synthesis has been suggested being based on the data array approximation by a set of elementary geometric algorithms that constructs a tree-like structure (the ACT model) for a preset initial training selection of arbitrary size. The latter consists of a set of autonomous classification/recognition algorithms assessed at each step of the ACT construction according to the initial selection. A method of the algorithmic classification tree construction has been developed with the basic idea of step-by-step arbitrary-volume and structure initial selection approximation by a set of elementary geometric classification algorithms. When forming a current algorithm tree vertex, node and generalized attribute, this method provides alignment of the most effective and high-quality elementary classification algorithms from the initial set and complete construction of only those paths in the ACT structure, where the most of classification errors occur. The scheme of synthesizing the resulting classification tree and the ACT model developed allows one to reduce considerably the tree size and complexity. The ACT construction structural complexity is being assessed on the basis of a number of transitions, vertices and tiers of the ACT structure that allows the quality of its further analysis to be increased, the efficient decomposition mechanism to be provided and the ACT structure to be built in conditions of fixed limitation sets. The algorithm tree synthesis method allows one to construct different-type tree-like recognition models with various sets of elementary classifiers at the preset accuracy for a wide class of artificial intelligence theory problems. Results. The method of discrete training selection approximation by a set of elementary geometric algorithms developed and presented in this work has received program realization and was studied and compared with those of logical tree classification on the basis of elementary attribute selection for solving the real geological data recognition problem. Conclusions. Both general analysis and experiments carried out in this work confirmed capability of developed mechanism of constructing the algorithm tree structures and demonstrate possibility of its promising use for solving a wide spectrum of applied recognition and classification problems. The outlooks of the further studies and approbations might be related to creating the othertype algorithmic classification tree methods with other initial sets of elementary classifiers, optimizing its program realizations, as well experimental studying this method for a wider circle of applied problems.

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Scalable Classification Tree Construction

Encyclopedia of Database Systems ◽

10.1007/978-0-387-39940-9_3513 ◽

2009 ◽

pp. 2468-2468

Keyword(s):

Classification Tree ◽

Tree Construction ◽

Scalable Classification

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Modular fault tree synthesis—A new method for computer-aided fault tree construction

Reliability Engineering ◽

10.1016/0143-8174(81)90031-7 ◽

1981 ◽

Vol 2 (1) ◽

pp. 65-76 ◽

Cited By ~ 7

Author(s):

A. Poucet ◽

P. De Meester

Keyword(s):

Fault Tree ◽

New Method ◽

Tree Construction ◽

Computer Aided ◽

Tree Synthesis

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Decision Tree Ensemble Techniques To Predict Thyroid Disease

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.c6727.098319 ◽

2019 ◽

Vol 8 (3) ◽

pp. 8242-8246 ◽

Cited By ~ 1

Keyword(s):

Decision Tree ◽

Classification Tree ◽

Real Data ◽

Classification Algorithm ◽

Ensemble Method ◽

Batch Size ◽

Classification Algorithms ◽

Ensemble Techniques ◽

Tree Algorithms ◽

Experimental Values

Decision tree provides help in making decision for very complex and large dataset. Decision tree techniques are used for gathering knowledge. Classification tree algorithms predict the experimental values of women thyroid dataset. The objective of this research paper observation is to determine hyperthyroidism, hypothyroidism and euthyroidism participation in hormones can be good predictor of the final result of laboratories and to examination whether the propose ensemble approach can be similar accuracy to other single classification algorithm. In the proposed experiment real data from 499 thyroid patients were used classifications algorithms in predicting whether thyroid detected or not detected on the basis of T3, T4 and TSH experimental values. The results show that the expectation of maximization classification tree algorithms in those of the best classification algorithm especially when using only a group of selected attributes. Finally we predict batch size, tree confidential factor, min number of observation, num folds, seed, accuracy and time build model with different classes of thyroid sickness. Different classification algorithms are analyzed using thyroid dataset. The results obtained by individual classification algorithms like J48, Random Tree and Hoeffding gives accuracy 99.12%, 97.59% and 92.37 respectively. Then we developed a new ensemble method and apply again on the same dataset, which gives a better accuracy of 99.2% and sensitivity of 99.36%. This new proposed ensemble method can be used for better classification of thyroid patients.

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Benthic Habitat Mapping Model and Cross Validation Using Machine-Learning Classification Algorithms

Remote Sensing ◽

10.3390/rs11111279 ◽

2019 ◽

Vol 11 (11) ◽

pp. 1279 ◽

Cited By ~ 8

Author(s):

Pramaditya Wicaksono ◽

Prama Ardha Aryaguna ◽

Wahyu Lazuardi

Keyword(s):

Machine Learning ◽

Classification Scheme ◽

Learning Algorithm ◽

Classification Tree ◽

Habitat Mapping ◽

Support Vector ◽

Benthic Habitat ◽

Classification Algorithms ◽

Machine Learning Classification ◽

Mapping Model

This research was aimed at developing the mapping model of benthic habitat mapping using machine-learning classification algorithms and tested the applicability of the model in different areas. We integrated in situ benthic habitat data and image processing of WorldView-2 (WV2) image to parameterise the machine-learning algorithm, namely: Random Forest (RF), Classification Tree Analysis (CTA), and Support Vector Machine (SVM). The classification inputs are sunglint-free bands, water column corrected bands, Principle Component (PC) bands, bathymetry, and the slope of underwater topography. Kemujan Island was used in developing the model, while Karimunjawa, Menjangan Besar, and Menjangan Kecil Islands served as test areas. The results obtained indicated that RF was more accurate than any other classification algorithm based on the statistics and benthic habitats spatial distribution. The maximum accuracy of RF was 94.17% (4 classes) and 88.54% (14 classes). The accuracies from RF, CTA, and SVM were consistent across different input bands for each classification scheme. The application of RF model in the classification of benthic habitat in other areas revealed that it is recommended to make use of the more general classification scheme in order to avoid several issues regarding benthic habitat variations. The result also established the possibility of mapping a benthic habitat without the use of training areas.

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Computer-aided fault tree synthesis II. Fault tree construction

Reliability Engineering & System Safety ◽

10.1016/0951-8320(91)90023-z ◽

1991 ◽

Vol 33 (1) ◽

pp. 1-21 ◽

Cited By ~ 13

Author(s):

Andre Bossche

Keyword(s):

Fault Tree ◽

Tree Construction ◽

Computer Aided ◽

Tree Synthesis

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Estimation of the complexity of constructing a logical classification tree for an arbitrary case in conditions of strong class separation of the initial training sample

Telecommunication and information technologies ◽

10.31673/2412-4338.2020.035566 ◽

2020 ◽

Vol 68 (3) ◽

Author(s):

I. F. Povkhan ◽

Keyword(s):

Decision Trees ◽

Classification Tree ◽

Classification Trees ◽

Training Sample ◽

Tree Structure ◽

Classification Model ◽

Initial Training ◽

Tree Models ◽

Logical Tree ◽

Selection Of

The paper offers an estimation of the complexity of the constructed logical tree structure for classifying an arbitrary case in the conditions of a strong class division of the initial training sample. The principal solution to this question is of a defining nature, regarding the assessment of the structural complexity of classification models (in the form of tree-like structures of LCT/ACT) of discrete objects for a wide range of applied classification and recognition problems in terms of developing promising schemes and methods for their final optimization (minimization) of post-pruning structure. The presented research is relevant not only for constructions (structures) of logical classification trees, but also allows us to extend the scheme of complexity estimation to the General case of algorithmic structures (ACT models) of classification trees (the concept of algorithm trees and trees of generalized features - TGF). Is investigated the actual question of the concept of decision trees (tree recognition) – evaluation of the maximum complexity of the General scheme of constructing a logical tree based classification procedure of stepwise selection of sets of elementary features (they can be diverse sets and combinations) that for given initial training sample (array of discrete information) builds a tree structure (classification model), from a set of elementary features (basic attributes) are estimated at each stage of the scheme of the model in this sample for the case of strong separation of classes. Modern information systems and technologies based on mathematical approaches (models) of pattern recognition (structures of logical and algorithmic classification trees) are widely used in socio-economic, environmental and other systems of primary analysis and processing of large amounts of information, and this is due to the fact that this approach allows you to eliminate a set of existing disadvantages of well-known classical methods, schemes and achieve a fundamentally new result. The research is devoted to the problems of classification tree models (decision trees), and offers an assessment of the complexity of logical tree structures (classification tree models), which consist of selected and ranked sets of elementary features (individual features and their combinations) built on the basis of the General concept of branched feature selection. This method, when forming the current vertex of the logical tree (node), provides the selection of the most informative (qualitative) elementary features from the source set. This approach allows you to significantly reduce the size and complexity of the tree (the total number of branches and tiers of the structure) and improve the quality of its subsequent instrumental analysis (the final decomposition of the model).

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AN INTEGRATED CLASSIFICATION-TREE METHODOLOGY FOR TEST CASE GENERATION

International Journal of Software Engineering and Knowledge Engineering ◽

10.1142/s0218194000000353 ◽

2000 ◽

Vol 10 (06) ◽

pp. 647-679 ◽

Cited By ~ 18

Author(s):

T. Y. CHEN ◽

P. L. POON ◽

T. H. TSE

Keyword(s):

Classification Tree ◽

Prototype System ◽

Test Case ◽

Test Cases ◽

Data Generation ◽

Generation System ◽

Integrated Methodology ◽

Tree Construction ◽

Automated Test Data Generation ◽

Tree Method

This paper describes an integrated methodology for the construction of test cases from functional specifications using the classification-tree method. It is an integration of our extensions to the classification-hierarchy table, the classification tree construction algorithm, and the classification tree restructuring technique. Based on the methodology, a prototype system ADDICT, which stands for AutomateD test Data generation system using the Integrated Classification-Tree method, has been built.

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Classification of Plant Ecological Units in Heterogeneous Semi-Steppe Rangelands: Performance Assessment of Four Classification Algorithms

Remote Sensing ◽

10.3390/rs13173433 ◽

2021 ◽

Vol 13 (17) ◽

pp. 3433

Author(s):

Masoumeh Aghababaei ◽

Ataollah Ebrahimi ◽

Ali Asghar Naghipour ◽

Esmaeil Asadi ◽

Jochem Verrelst

Keyword(s):

Accuracy Assessment ◽

Classification Tree ◽

Principal Component ◽

Classification Algorithms ◽

Landsat 8 ◽

Auxiliary Data ◽

Management Actions ◽

Object Based ◽

Significant Difference ◽

Plant Ecological

Plant Ecological Unit’s (PEUs) are the abstraction of vegetation communities that occur on a site which similarly respond to management actions and natural disturbances. Identification and monitoring of PEUs in a heterogeneous landscape is the most difficult task in medium resolution satellite images datasets. The main objective of this study is to compare pixel-based classification versus object-based classification for accurately classifying PEUs with four selected different algorithms across heterogeneous rangelands in Central Zagros, Iran. We used images of Landsat-8 OLI that were pan-sharpened to 15 m to classify four PEU classes based on a random dataset collected in the field (40%). In the first stage, we applied the following classification algorithms to distinguish PEUs: Minimum Distance (MD), Maximum Likelihood Classification (MLC), Neural Network-Multi Layer Perceptron (NN-MLP) and Classification Tree Analysis (CTA) for pixel based method and object based method. Then, by using the most accurate classification approach, in the second stage auxiliary data (Principal Component Analysis (PCA)) was incorporated to improve the accuracy of the PEUs classification process. At the end, test data (60%) were used for accuracy assessment of the resulting maps. Object-based maps clearly outperformed pixel-based maps, especially with CTA, NN-MLP and MD algorithms with overall accuracies of 86%, 72% and 59%, respectively. The MLC algorithm did not reveal any significant difference between the object-based and pixel-based analyses. Finally, complementing PCA auxiliary bands to the CTA algorithms offered the most successful PEUs classification strategy, with the highest overall accuracy (89%). The results clearly underpin the importance of object-based classification with the CTA classifier together with PCA auxiliary data to optimize identification of PEU classes.

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