Comments on the hierarchically structured bin packing problem

We study the hierarchically structured bin packing problem. In this problem, the items to be packed into bins are at the leaves of a tree. The objective of the packing is to minimize the total number of bins into which the descendants of an internal node are packed, summed over all internal nodes. We investigate an existing algorithm and make a correction to the analysis of its approximation ratio. Further results regarding the structure of an optimal solution and a strengthened inapproximability result are given.     

A new encoding technique for peptide classification

Research on peptide classification problems has focused mainly on the study of different encodings and the application of several classification algorithms to achieve improved prediction accuracies. The main drawback of the literature is the lack of an extensive comparison among the available encoding methods on a wide range of classification problems. This paper addresses the fundamental issue of which peptide encoding promises the best results for machine learning classifiers. Two novel encoding methods based on physicochemical properties of the amino acids are proposed and an extensive comparison with several standard encoding methods is performed on three different classification problems (HIV-protease, recognition of T-cell epitopes and prediction of peptides that bind human leukocyte antigens). The experimental results demonstrate the effectiveness of the new encodings and show that the frequently used orthonormal encoding is inferior compared to other methods.     

Advanced machine learning techniques for microarray spot quality classification

It is well known that microarray printing, hybridization, and washing oftentimes create erroneous measurements, and these errors detrimentally impact machine microarray spot quality classification. Thus, it is crucial to identify and remove these errors if automation is to replace the still common practice of visually assessing spot quality, an extremely expensive and time-consuming procedure. A major problem in microarray spot quality classification methods proposed in the literature is the correlation among the features extracted from the spots. In this paper, we propose using a random subspace ensemble of neural networks and a feature selection algorithm to improve the performance of our microarray spot quality classification method. Our best method obtains an error under the receiver operating characteristic curve (EAUR) of 0.3 outperforming the stand-alone support vector machine EAUR of 1.7. The consistency of our proposed approach makes it a viable alternative to the labour-intensive manual method of spot quality assessment.     

A genetic encoding approach for learning methods for combining classifiers

Several studies have reported that the ensemble of classifiers can improve the performance of a stand-alone classifier. In this paper, we propose a learning method for combining the predictions of a set of classifiers.The method described in this paper uses a genetic-based version of the correspondence analysis for combining classifiers. The correspondence analysis is based on the orthonormal representation of the labels assigned to the patterns by a pool of classifiers. In this paper instead of the orthonormal representation we use a pool of representations obtained by a genetic algorithm. Each single representation is used to train a different classifiers, these classifiers are combined by vote rule.The performance improvement with respect to other learning-based fusion methods is validated through experiments with several benchmark datasets.     

Safer plasticized polyvinyl chloride synthetic leathers for the automotive industry: Evaluation of alternatives to antimony compounds as flame retardants

Automotive interiors materials, like plasticized polyvinyl chloride (pPVC) synthetic leathers (SLs), require additives for improving their flame behavior. The preferred flame retardant (FR) used in pPVC is antimony trioxide (Sb₂O₃, ATO), though the use of antimony poses several issues, for both human health and the environment, related to its extraction, processing, and use. In order to investigate alternatives to ATO in high‐performance pPVC SLs, various commercial FRs have been selected and tested in a typical, highly plasticized formulation. These additives have been used either alone or combined to evaluate synergistic effects. Samples have been tested to assess mechanical properties, thermal stability, and flame resistance. Data have been compared with those of neat pPVC and a foil with 2 phr of ATO. Several FRs are effective in improving the flame response compared with neat pPVC, without compromising the other properties, in detail calcium hypophosphite and mixtures containing zinc hydroxystannate (ZHS). Finally, aluminum hydroxide and ZHS (ATH + ZHS) yields the cheaper among the alternatives here proposed, even though higher than ATO (+193%) whose price/performance ratio is difficult to overcome. POLYM. ENG. SCI., 59:2488–2497, 2019. © 2019 Society of Plastics Engineers