A review on the combination of binary classifiers in multiclass problems

Several real problems involve the classification of data into categories or classes. Given a data set containing data whose classes are known, Machine Learning algorithms can be employed for the induction of a classifier able to predict the class of new data from the same domain, performing the desired discrimination. Some learning techniques are originally conceived for the solution of problems with only two classes, also named binary classification problems. However, many problems require the discrimination of examples into more than two categories or classes. This paper presents a survey on the main strategies for the generalization of binary classifiers to problems with more than two classes, known as multiclass classification problems. The focus is on strategies that decompose the original multiclass problem into multiple binary subtasks, whose outputs are combined to obtain the final prediction.

Multilevel Training of Binary Morphological Operators

The design of binary morphological operators that are translation-invariant and locally defined by a finite neighborhood window corresponds to the problem of designing Boolean functions. As in any supervised classification problem, morphological operators designed from a training sample also suffer from overfitting. Large neighborhood tends to lead to performance degradation of the designed operator. This work proposes a multilevel design approach to deal with the issue of designing large neighborhood-based operators. The main idea is inspired by stacked generalization (a multilevel classifier design approach) and consists of, at each training level, combining the outcomes of the previous level operators. The final operator is a multilevel operator that ultimately depends on a larger neighborhood than of the individual operators that have been combined. Experimental results show that two-level operators obtained by combining operators designed on subwindows of a large window consistently outperform the single-level operators designed on the full window. They also show that iterating two-level operators is an effective multilevel approach to obtain better results.

Ability of Ru complexes for ROMP tuned through a combination of phosphines and amines

Polynorbonerne with high molecular weight was obtained via ring opening metathesis polymerization using catalysts derived from [RuCl(2)(PPh(2)Bz)(2) L] (1 for L = PPh(2) Bz; 2 for L = piperidine) type of complexes when in the presence of ethyl diazoacetate in CHCl(3). The polymer precipitated within a few minutes at 50 degrees C when using 1 with ca. 50% yield ([NBE]/[Ru] = 5000). Regarding 2, for either 30 min at 25 C or 5 min at 50 degrees C, more than 90% of yields are obtained; and at 50 C for 30 min a quantitative yield is obtained. The yield and PDI values are sensitive to the [NBE]/[Ru] ratio. The reaction of 1 with either isonicotinamide or nicotinamide produces six-coordinated complexes of [RuCl(2)(PPh(2)Bz)(2)(L)(2)] type, which are almost inactive and produce only small amounts of polymers at 50 C for 30 min. Thus, we Concluded that the novel complexes show very distinct reactivities for ROMP of NBE. This has been rationalized on account of a combination of synergistic effects of the phosphine-amine ancillary ligands. (C) 2009 Elsevier B.V. All rights reserved.