SPAARC: A Fast Decision Tree Algorithm

Yates, Darren; Islam, Zahidul; Gao, Junbin

doi:10.1007/978-981-13-6661-1_4

Cited by 13 publications

(13 citation statements)

References 5 publications

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“…As previously mentioned, Reduced Error Pruning Tree acts a fast decision tree learning as well as it makes a decision tree according to the data gain and can reduce the variance. SPAARC is a classifier that utilizes Simple CART (Yates et al 2018). SPAARC includes two components to overcome problems of decision trees.…”

Section: Classification Techniquesmentioning

confidence: 99%

Evaluation of tree-base data mining algorithms in land used/land cover mapping in a semi-arid environment through Landsat 8 OLI image; Shiraz, Iran

Moayedi

Jamali

Gibril

et al. 2020

Geomatics, Natural Hazards and Risk

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Land Use Land Cover (LULC) mapping has been used in different environmental applications including disaster management, risk analysis, heat island mapping, and the effects of urbanization on environmental changes such as floods and droughts in the recent decade. The earth's natural surface coverage including urban infrastructure, surface vegetation, bare soil can be identified with LULC maps. Besides, LULC is one of the most important tasks in natural hazards, planning activities, resource management, and global monitoring studies. The present study aimed to improve the level of land used and land cover change mapping in a semi-arid environment using Landsat 8 Operational Land Imager (OLI) image. In this research, several tree-based algorithms for LULC mapping are used and compared. These algorithms are used in a combination model implementation of WEKA 3.8 and R programming language to provide a method of a fit-for-purpose algorithms for LULC mapping. It is found that Reduced Error Pruning Tree (REP Tree) is the best approach according to the training and test datasets which are four classes including the build-up, soil, roads, and vegetation region pixels in a semi-arid environment. For the training dataset, the best values of Overall Accuracy (OA), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) is determined, 99.7313, 0.002 and 0.0354 as well as for the test dataset are 99.7313, 0.002 and 0.0354, respectively. Finally, trained models of the implemented data mining algorithms are used for the classification of a different dataset without using any training data where the Logical Analysis of Data Tree (LAD Tree) algorithm shows the best performance in terms of OA, MAE, and RMSE with values of 99.2815, 0.0057, and 0.0557 respectively.

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Section: Classification Techniquesmentioning

confidence: 99%

Evaluation of tree-base data mining algorithms in land used/land cover mapping in a semi-arid environment through Landsat 8 OLI image; Shiraz, Iran

Moayedi

Jamali

Gibril

et al. 2020

Geomatics, Natural Hazards and Risk

View full text Add to dashboard Cite

show abstract

“…Thus, to take advantage of parallelisation, optimisations originating within the base decision tree itself appear to offer the best potential. Previous examples of in-tree optimisations include the SPAARC decision tree algorithm [15] that incorporates a technique called 'Split-Point Sampling' (SPS). This single-tree algorithm follows standard tree induction practices by testing attributes at each node to determine which one creates the most homogenous split of records.…”

Section: Speed Optimisations Beyond Parallel Processingmentioning

confidence: 99%

“…This single-tree algorithm follows standard tree induction practices by testing attributes at each node to determine which one creates the most homogenous split of records. However, the Split Point Sampling (SPS) technique presented in SPAARC [15] reduces the number of potential split-point candidates for numerical attribute values to a maximum count of 20, equi-distant across the attribute value range. It was shown empirically that SPS reduces processing time without significantly affecting the overall classification accuracy of the decision tree.…”

Section: Speed Optimisations Beyond Parallel Processingmentioning

confidence: 99%

FastForest: Increasing Random Forest Processing Speed While Maintaining Accuracy

Yates¹,

Islam²

2020

Preprint

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Random Forest remains one of Data Mining's most enduring ensemble algorithms, achieving welldocumented levels of accuracy and processing speed, as well as regularly appearing in new research. However, with data mining now reaching the domain of hardware-constrained devices such as smartphones and Internet of Things (IoT) devices, there is continued need for further research into algorithm efficiency to deliver greater processing speed without sacrificing accuracy. Our proposed FastForest algorithm delivers an average 24% increase in processing speed compared with Random Forest whilst maintaining (and frequently exceeding) it on classification accuracy over tests involving 45 datasets. FastForest achieves this result through a combination of three optimising components -Subsample Aggregating ('Subbagging'), Logarithmic Split-Point Sampling and Dynamic Restricted Subspacing. Moreover, detailed testing of Subbagging sizes has found an optimal scalar delivering a positive mix of processing performance and accuracy.

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“…In addition to the collection of built-in algorithms discussed in Section 3.2, the DataLearner source code has been designed to also allow external algorithms to be included and compiled into the application. This process has enabled the addition of three further classifiers developed by researchers at Charles Sturt University, namely SysFor [23], ForestPA [24] and SPAARC [25]. This is achieved by replicating the standard Weka source folder structure within the DataLearner Java application folder.…”

Section: Adding External Algorithmsmentioning

confidence: 99%

“…SPAARC (Split-Point and Attribute-Reduction Classifier) [25] is a single-tree classification algorithm based on the popular CART (Classification And Regression Tree) algorithm [26]. It incorporates techniques for reducing the computational load, improving processing time whilst minimising effects on classification accuracy.…”

Section: Sysfor Forestpa and Spaarcmentioning

confidence: 99%

DataLearner: A Data Mining and Knowledge Discovery Tool for Android Smartphones and Tablets

Yates

Islam

Gao

2019

Advanced Data Mining and Applications

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Smartphones have become the ultimate 'personal' computer, yet despite this, general-purpose data mining and knowledge discovery tools for mobile devices are surprisingly rare. DataLearner is a new data mining application designed specifically for Android devices that imports the Weka data mining engine and augments it with algorithms developed by Charles Sturt University. Moreover, DataLearner can be expanded with additional algorithms. Combined, DataLearner delivers 40 classification, clustering and association rule mining algorithms for model training and evaluation without need for cloud computing resources or network connectivity. It provides the same classification accuracy as PCs and laptops, while doing so with acceptable processing speed and consuming negligible battery life. With its ability to provide easy-to-use data mining on a phone-size screen, DataLearner is a new portable, self-contained data mining tool for remote, personalised and educational applications alike. DataLearner features four elements -this paper, the app available on Google Play, the GPL3-licensed source code on GitHub and a short video on YouTube.

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SPAARC: A Fast Decision Tree Algorithm

Cited by 13 publications

References 5 publications

Evaluation of tree-base data mining algorithms in land used/land cover mapping in a semi-arid environment through Landsat 8 OLI image; Shiraz, Iran

Evaluation of tree-base data mining algorithms in land used/land cover mapping in a semi-arid environment through Landsat 8 OLI image; Shiraz, Iran

FastForest: Increasing Random Forest Processing Speed While Maintaining Accuracy

DataLearner: A Data Mining and Knowledge Discovery Tool for Android Smartphones and Tablets

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