Theoretical Foundations and Experimental Results for a Hierarchical Classifier With Overlapping Clusters

Podolak, Igor T.; Roman, Adam

doi:10.1111/j.1467-8640.2012.00469.x

Cited by 8 publications

(7 citation statements)

References 34 publications

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“…OLS assumes that each observation has the same variance; as a result, it is aimed at the maximization of the accuracy rather than any balanced metric. However, there are many approaches that can address imbalanced datasets (for both binary classification [33] and multi-class classification [34]). In particular it is possible to use the weighted least squares method for ELMs, which has already been applied in the past [32].…”

Section: E Class Balancingmentioning

confidence: 99%

Weighted Tanimoto Extreme Learning Machine with Case Study in Drug Discovery

Czarnecki

2015

IEEE Comput. Intell. Mag.

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Machine learning methods are becoming more and more popular in the field of computer-aided drug design. The specific data characteristic, including sparse, binary representation as well as noisy, imbalanced datasets, presents a challenging binary classification problem. Currently, two of the most successful models in such tasks are the Support Vector Machine (SVM) and Random Forest (RF). In this paper, we introduce a Weighted Tanimoto Extreme Learning Machine (T-WELM), an extremely simple and fast method for predicting chemical compound biological activity and possibly other data with discrete, binary representation. We show some theoretical properties of the proposed model including the ability to learn arbitrary sets of examples. Further analysis shows numerous advantages of T-WELM over SVMs, RFs and traditional Extreme Learning Machines (ELM) in this particular task. Experiments performed on 40 large datasets of thousands of chemical compounds show that T-WELMs achieve much better classification results and are at the same time faster in terms of both training time and further classification than both ELM models and other state-of-the-art methods in the field.

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Section: E Class Balancingmentioning

confidence: 99%

Weighted Tanimoto Extreme Learning Machine with Case Study in Drug Discovery

Czarnecki

2015

IEEE Comput. Intell. Mag.

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“…Our approach belongs to the hybrid approaches [6,10] which typically try to combine supervised and unsupervised techniques in one, uniform model. Some of these methods are combinations of clustering and classification techniques in either direct form [5] or using the complex, hierarchical structures of alternating algorithms [11].…”

Section: Introductionmentioning

confidence: 99%

Cluster based RBF Kernel for Support Vector Machines

Czarnecki,

Tabor

2014

Preprint

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In the classical Gaussian SVM classification we use the feature space projection transforming points to normal distributions with fixed covariance matrices (identity in the standard RBF and the covariance of the whole dataset in Mahalanobis RBF). In this paper we add additional information to Gaussian SVM by considering local geometry-dependent feature space projection. We emphasize that our approach is in fact an algorithm for a construction of the new Gaussiantype kernel.We show that better (compared to standard RBF and Mahalanobis RBF) classification results are obtained in the simple case when the space is preliminary divided by k-means into two sets and points are represented as normal distributions with a covariances calculated according to the dataset partitioning. We call the constructed method C k RBF, where k stands for the amount of clusters used in k-means. We show empirically on nine datasets from UCI repository that C 2 RBF increases the stability of the grid search (measured as the probability of finding good parameters).

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“…Dedicated combination method, such as Error-Correcting Output Codes [25], is being used to reconstruct original multi-class task. Most notable examples of this type of technique include binarization [13], hierarchical decomposition [27] and classifier chains [22].…”

Section: Related Workmentioning

confidence: 99%