Data selection based on decision tree for SVM classification on large data sets

Cervantes, Jair; García-Lamont, Farid; López-Chau, Asdrúbal; Rodríguez-Mazahua, Lisbeth; Ruiz, Jose

doi:10.1016/j.asoc.2015.08.048

Cited by 69 publications

(28 citation statements)

References 43 publications

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“…The authors showed that their algorithm is competitive to four state-of-the-art techniques and is applicable to other classifiers. In a recent paper, Cervantes et al (2015) incorporated an induction tree to reduce the size of SVM training sets. The main idea behind the proposed technique is to train SVMs using significantly smaller refined training sets, and then to label vectors from T as those which are close or far from the decision hyperplane.…”

Section: Neighborhood Analysis Methodsmentioning

confidence: 99%

Selecting training sets for support vector machines: a review

2018

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Support vector machines (SVMs) are a supervised classifier successfully applied in a plethora of real-life applications. However, they suffer from the important shortcomings of their high time and memory training complexities, which depend on the training set size. This issue is especially challenging nowadays, since the amount of data generated every second becomes tremendously large in many domains. This review provides an extensive survey on existing methods for selecting SVM training data from large datasets. We divide the state-of-the-art techniques into several categories. They help understand the underlying ideas behind these algorithms, which may be useful in designing new methods to deal with this important problem. The review is complemented with the discussion on the future research pathways which can make SVMs easier to exploit in practice.

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Section: Neighborhood Analysis Methodsmentioning

confidence: 99%

Selecting training sets for support vector machines: a review

2018

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“…The hyperplanes themselves are vectors with linear properties. As the dot product computation in larger dimensional space could be complex, the kernel function is defined to address this issue [59,60].…”

Section: Support Vector Machinesmentioning

confidence: 99%

Comparative Study on KNN and SVM Based Weather Classification Models for Day Ahead Short Term Solar PV Power Forecasting

et al. 2017

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Accurate solar photovoltaic (PV) power forecasting is an essential tool for mitigating the negative effects caused by the uncertainty of PV output power in systems with high penetration levels of solar PV generation. Weather classification based modeling is an effective way to increase the accuracy of day-ahead short-term (DAST) solar PV power forecasting because PV output power is strongly dependent on the specific weather conditions in a given time period. However, the accuracy of daily weather classification relies on both the applied classifiers and the training data. This paper aims to reveal how these two factors impact the classification performance and to delineate the relation between classification accuracy and sample dataset scale. Two commonly used classification methods, K-nearest neighbors (KNN) and support vector machines (SVM) are applied to classify the daily local weather types for DAST solar PV power forecasting using the operation data from a grid-connected PV plant in Hohhot, Inner Mongolia, China. We assessed the performance of SVM and KNN approaches, and then investigated the influences of sample scale, the number of categories, and the data distribution in different categories on the daily weather classification results. The simulation results illustrate that SVM performs well with small sample scale, while KNN is more sensitive to the length of the training dataset and can achieve higher accuracy than SVM with sufficient samples.

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“…Both the SVM and RF algorithms are widely used for classification. SVM uses different types of points to map to high-dimensional space to construct hyperplane separations, mainly to remedy low-dimensional linear inseparability issues and achieve good performance with feature selection [42][43][44]. RF is a classical ensemble algorithm that constructs a random tree by sampling data and features.…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Grid-Based Crime Prediction Using Geographical Features

Lin

Yen

2018

IJGI

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Machine learning is useful for grid-based crime prediction. Many previous studies have examined factors including time, space, and type of crime, but the geographic characteristics of the grid are rarely discussed, leaving prediction models unable to predict crime displacement. This study incorporates the concept of a criminal environment in grid-based crime prediction modeling, and establishes a range of spatial-temporal features based on 84 types of geographic information by applying the Google Places API to theft data for Taoyuan City, Taiwan. The best model was found to be Deep Neural Networks, which outperforms the popular Random Decision Forest, Support Vector Machine, and K-Near Neighbor algorithms. After tuning, compared to our design's baseline 11-month moving average, the F1 score improves about 7% on 100-by-100 grids. Experiments demonstrate the importance of the geographic feature design for improving performance and explanatory ability. In addition, testing for crime displacement also shows that our model design outperforms the baseline.

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Data selection based on decision tree for SVM classification on large data sets

Cited by 69 publications

References 43 publications

Selecting training sets for support vector machines: a review

Selecting training sets for support vector machines: a review

Comparative Study on KNN and SVM Based Weather Classification Models for Day Ahead Short Term Solar PV Power Forecasting

Grid-Based Crime Prediction Using Geographical Features

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