A support vector machine approach to decision trees

Bennett, Kristin P.; Blue, Jennifer A.

doi:10.1109/ijcnn.1998.687237

Cited by 139 publications

(97 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A few decades ago, a basic SVM approach was used to interrogate the WBCD [81] and 97.2% classification accuracy was obtained. The result has proved the basic SVM approach achieved a high accuracy given quality data in BC.…”

Section: Svmsmentioning

confidence: 99%

Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis

Yue

Wang

Chen

et al. 2018

Designs

180

View full text Add to dashboard Cite

Breast cancer (BC) is one of the most common cancers among women worldwide, representing the majority of new cancer cases and cancer-related deaths according to global statistics, making it a significant public health problem in today's society. The early diagnosis of BC can improve the prognosis and chance of survival significantly, as it can promote timely clinical treatment to patients. Further accurate classification of benign tumours can prevent patients undergoing unnecessary treatments. Thus, the correct diagnosis of BC and classification of patients into malignant or benign groups is the subject of much research. Because of its unique advantages in critical features detection from complex BC datasets, machine learning (ML) is widely recognised as the methodology of choice in BC pattern classification and forecast modelling. In this paper, we aim to review ML techniques and their applications in BC diagnosis and prognosis. Firstly, we provide an overview of ML techniques including artificial neural networks (ANNs), support vector machines (SVMs), decision trees (DTs), and k-nearest neighbors (k-NNs). Then, we investigate their applications in BC. Our primary data is drawn from the Wisconsin breast cancer database (WBCD) which is the benchmark database for comparing the results through different algorithms. Finally, a healthcare system model of our recent work is also shown.

show abstract

Section: Svmsmentioning

confidence: 99%

Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis

Yue

Wang

Chen

et al. 2018

Designs

180

View full text Add to dashboard Cite

show abstract

“…Both Bennett and Blue [15], and Madzarov et.al. [16] use decision trees to reduce the number of support vectors, in an effort to reduce the computational cost of executing an SVM classifier given the feature values in a multi-class setting.…”

Section: Cost Efficient Svmmentioning

confidence: 99%

ACE-Cost: Acquisition Cost Efficient Classifier by Hybrid Decision Tree with Local SVM Leaves

Topkara

Memon

2011

Machine Learning and Data Mining in Pattern Recognition

View full text Add to dashboard Cite

Abstract. The standard prediction process of SVM requires acquisition of all the feature values for every instance. In practice, however, a cost is associated with the mere act of acquisition of a feature, e.g. CPU time needed to compute the feature out of raw data, the dollar amount spent for gleaning more information, or the patient wellness sacrificed by an invasive medical test, etc. In such applications, a budget constrains the classification process from using all of the features. We present, AceCost, a novel classification method that reduces the expected test cost of SVM without compromising from the classification accuracy. Our algorithm uses a cost efficient decision tree to partition the feature space for obtaining coarse decision boundaries, and local SVM classifiers at the leaves of the tree to refine them. The resulting classifiers are also effective in scenarios where several features share overlapping acquisition procedures, hence the cost of acquiring them as a group is less than the sum of the individual acquisition costs. Our experiments on the standard UCI datasets, a network flow detection application, as well as on synthetic datasets show that, the proposed approach achieves classification accuracy of SVM while reducing the test cost by 40%-80%.

show abstract

“…In this paper, we depart from this by allowing different solutions in different regions of the feature space. The only works we know of that use this idea are [28] and [2]. Especially [2] discusses a method that seems similar to the one presented here as it combines decision trees with support vector machines.…”

Section: Related Workmentioning

confidence: 99%

“…The only works we know of that use this idea are [28] and [2]. Especially [2] discusses a method that seems similar to the one presented here as it combines decision trees with support vector machines. However, the main motivation in [2] is not to reduce the number of kernel evaluations, and the resulting algorithm is a fairly complex combination of gradient descent and tabu search.…”

Section: Related Workmentioning

confidence: 99%

“…Especially [2] discusses a method that seems similar to the one presented here as it combines decision trees with support vector machines. However, the main motivation in [2] is not to reduce the number of kernel evaluations, and the resulting algorithm is a fairly complex combination of gradient descent and tabu search. In contrast to these, the work proposed here is done in the context of online learning with kernel perceptrons.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Example-dependent Basis Vector Selection for Kernel-Based Classifiers

Ukkonen

Arias

2010

Machine Learning and Knowledge Discovery in Databases

View full text Add to dashboard Cite

Abstract. We study methods for speeding up classification time of kernel-based classifiers. Existing solutions are based on explicitly seeking sparse classifiers during training, or by using budgeted versions of the classifier where one directly limits the number of basis vectors allowed. Here, we propose a more flexible alternative: instead of using the same basis vectors over the whole feature space, our solution uses different basis vectors in different parts of the feature space. At the core of our solution lies an optimization procedure that, given a set of basis vectors, finds a good partition of the feature space and good subsets of the existing basis vectors. Using this procedure repeatedly, we build trees whose internal nodes specify feature space partitions and whose leaves implement simple kernel classifiers. Experiments suggest that our method reduces classification time significantly while maintaining performance. In addition, we propose several heuristics that also perform well.

show abstract

A support vector machine approach to decision trees

Cited by 139 publications

References 4 publications

Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis

Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis

ACE-Cost: Acquisition Cost Efficient Classifier by Hybrid Decision Tree with Local SVM Leaves

Example-dependent Basis Vector Selection for Kernel-Based Classifiers

Contact Info

Product

Resources

About