Classifier Ensemble Recommendation

Matikainen, Pyry; Sukthankar, Rahul; Hebert, Martial

doi:10.1007/978-3-642-33863-2_21

Cited by 8 publications

(2 citation statements)

References 11 publications

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“…Some works [19], [20], [21], [22], [23] provide an overview on study and recommendations for ensemble based techniques which improve the classification accuracy compared to that of a single classifier. Jasmina et al used AdaBoost Classifier ensemble in his paper [24].…”

Section: A Existing Methods In the Literaturementioning

confidence: 99%

Ensemble based classification using small training sets : A novel approach

Veni¹,

Rani²

2014

2014 IEEE Symposium on Computational Intelligence in Ensemble Learning (CIEL)

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Classification is a supervised learning technique typically uses two-thirds of the given annotated data set for training and the remaining for test. In this paper, we developed a frame work which uses less than one-third of the data set for training and tests the remaining two-thirds of the data and still gives results comparable to other classifiers. To achieve good classification accuracy with small training sets, we focused on three issues: The first is that, one-third(30%) of the data should represent the entire data set. The second is on increasing the classification accuracy even with these small training sets, and the third issue is on taking care of deviations in the small training sets like noise or outliers. First issue is addressed by proposing three methods: divide the instances into 10 bins based on their distances from the centroid, based on their distance from a reference point 3/2(min+max) and a distribution specific binning. In all these methods, training sets are formed using stratified sampling approach which ensures that the samples chosen are from the entire distribution. Second issue is dealt with using the concept of ensemble based weighted majority voting for classification. Third issue is tackled by implementing four filters on training sets. The filters used are Removing Outliers using Inter Quartile Range option (available in Weka) and removing misclassified instances applying Naive Bayes, IB3, IB5 as filters. Experiments are conducted on seven binary and multi-class data sets taking only 6% to 18% of the total data for training and implemented the proposed three methods without any filters for noise and outlier removal and with them too on the training sets. We compare our results with two popular ensemble methods ada-boost and bagging ensemble techniques, ENN, CNN, RNN instance selection methods. Empirical analysis shows that our three proposed methods yield comparable classification results to those available in literature which use small training sets.

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Section: A Existing Methods In the Literaturementioning

confidence: 99%

Ensemble based classification using small training sets : A novel approach

Veni¹,

Rani²

2014

2014 IEEE Symposium on Computational Intelligence in Ensemble Learning (CIEL)

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“…For example, [21] which uses k-means to cluster the data, and then find best classifiers for each cluster. Dynamic methods [14,27] on the other hand, use test time information to do this selection, e.g., recommender systems [27] rely on probing the test set to provide the final ensemble. Our method follows the former category as all the computation is done offline without any test time information.…”

Section: Related Workmentioning

confidence: 99%

Data-driven exemplar model selection

Misra

Shrivastava

Hebert

2014

IEEE Winter Conference on Applications of Computer Vision

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We consider the problem of discovering discriminative exemplars suitable for object detection. Due to the diversity in appearance in real world objects, an object detector must capture variations in scale, viewpoint, illumination etc. The current approaches do this by using mixtures of models, where each mixture is designed to capture one (or a few) axis of variation. Current methods usually rely on heuristics to capture these variations; however, it is unclear which axes of variation exist and are relevant to a particular task. Another issue is the requirement of a large set of training images to capture such variations. Current methods do not scale to large training sets either because of training time complexity [31] or test time complexity [26]. In this work, we explore the idea of compactly capturing taskappropriate variation from the data itself. We propose a two stage data-driven process, which selects and learns a compact set of exemplar models for object detection. These selected models have an inherent ranking, which can be used for anytime/budgeted detection scenarios. Another benefit of our approach (beyond the computational speedup) is that the selected set of exemplar models performs better than the entire set.

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