A novel combining classifier method based on Variational Inference

Sharma

et al. 2019

Information Sciences

Self Cite

We are living in a world progressively driven by data. Besides mining big data which cannot be altogether stored in the main memory as required by traditional offline methods, the problem of learning rare data that can only be collected over time is also very prevalent. Consequently, there is a need of online methods which can handle arriving data and offer the same accuracy as offline methods. In this paper, we introduce a new lossless online Bayesianbased classifier which uses the arriving data in a 1-by-1 manner and discards each data right after use. The lossless property of our proposed method guarantees that it can reach the same prediction model as its offline counterpart regardless of the incremental training order. Experimental results demonstrate its superior performance over many well-known state-of-theart methods in the literature.

Section: A Novel Lossless Bayesian Methodsmentioning

confidence: 99%

A lossless online Bayesian classifier

Sharma

et al. 2019

Information Sciences

Self Cite

“…These datasets are often used to assess the performance of classification systems [48]. Abalone 8 4174 3 Artificial 10 700 2 Australian 14 690 2 Blood 4 748 2 Bupa 6 345 2 Contraceptive 9 1473 3 Dermatology 34 358 6 Fertility 9 100 2 Haberman 3 306 2 Heart 13 270 2 Penbased 16 10992 10 Pima 8 768 2 Plant Margin 64 1600 100 Satimage 36 6435 6 Skin_NonSkin 3 245057 2 Tae 20 151 3 Texture 40 5500 10 Twonorm 20 7400 2 Vehicle 18 946 4 Vertebral 6 310 3 Yeast 8 1484 10 We performed extensive comparative studies with a number of existing algorithms as Tree with maximum of 200 iterations as in [19]), Bagging [21], and Random Subspace [23] (we used 200 learners as in [19]).…”

Section: Datasets and Experimental Settingsmentioning

confidence: 99%

Aggregation of Classifiers: A Justifiable Information Granularity Approach

Pham

Liew

et al. 2019

IEEE Trans. Cybern.

Self Cite

In this paper, we introduced a new approach of combining multiple classifiers in a heterogeneous ensemble system. Instead of using numerical membership values when combining, we constructed interval membership values for each class prediction from the meta-data of observation by using the concept of information granule. In the proposed method, the uncertainty (diversity) of the predictions produced by the base classifiers is quantified by the interval-based information granules. The decision model is then generated by considering both bound and length of the intervals. Extensive experimentation using the UCI datasets has demonstrated the superior performance of our algorithm over other algorithms including six fixed combining methods, one trainable combining method, AdaBoost, bagging, and random subspace.

“…Ensemble methods have been shown to achieve higher classification accuracy than single classifier systems and have been applied to many applications such as object detection and tracking, computer-aided medical diagnosis, and intrusion detection [48]. In general, ensemble methods can be categorized into two types [27]:…”

Section: Introductionmentioning

confidence: 99%

A weighted multiple classifier framework based on random projection

Dang

Liew

et al. 2019

Information Sciences

Self Cite

In this paper, we propose a weighted multiple classifier framework based on random projections. Similar to the mechanism of other homogeneous ensemble methods, the base classifiers in our approach are obtained by a learning algorithm on different training sets generated by projecting the original up-space training set to lower dimensional down-spaces. We then apply a Least Squarebased method to weigh the outputs of the base classifiers so that the contribution of each classifier to the final combined prediction is different. We choose Decision Tree as the learning algorithm in the proposed framework and conduct experiments on a number of real and synthetic datasets. The experimental results indicate that our framework is better than many of the benchmark algorithms, including three homogeneous ensemble methods (Bagging, RotBoost, and Random Subspace), several well-known algorithms (Decision Tree, Random Neural Network, Linear Discriminative Analysis, K Nearest Neighbor, L2-loss Linear Support Vector Machine, and Discriminative Restricted Boltzmann Machine), and random projection-based ensembles with fixed combining rules with regard to both classification error rates and F1 scores.