A dissimilarity-based classifier for generalized sequences by a granular computing approach

Abstract

In this paper we propose a classifier for generalized sequences that is conceived in the granular computing framework. The classification system processes the input sequences of objects by means of a suited interplay among dissimilarity and clustering based techniques. The core data mining engine retrieves information granules that are used to represent the input sequences as feature vectors. Such a representation allows to deal with the original sequence classification problem through standard pattern recognition tools. We have evaluated the generalization capability of the system in an interesting case study concerning the protein folding problem. In the considered dataset, the entire E. Coli proteome was screened as for the prediction of protein relative solubility on a pure amino acids sequence basis. We report the analysis of the dataset considering different settings, showing interesting test set classification accuracy results. The developed system consents also to extract knowledge from the considered training set, by allowing the analysis of the retrieved information granules. © 2013 IEEE

Interval Type-2 Fuzzy Set Reconstruction Based on Fuzzy Information-Theoretic Kernels

“…Such measures constitute the key element in the construction of data-driven modeling systems, especially when dealing with the so-called nongeometric input spaces [33]- [35]. Typical examples of the related practical applications that deal directly with such patterns include images [36], audio/video signals [37], and biochemical compounds [38]. In such cases, where the geometry of the input space D is not obvious, the role of similarity measures is to provide a way to measure the commonalities among the elements of D. Formally, a similarity measure [39] on D is a bounded (usually nonnegative) function of two arguments s : D × D → R, such that…”

Classifying sequences by the optimized dissimilarity space embedding approach: A case study on the solubility analysis of the E. coli proteome

“…2(a)). For instance, when considering [0, 0.3] and [0.7, 1] as the two intervals of normalized solubility characterizing the insoluble and soluble proteins, the dataset would be split into 1631 insoluble and 180 soluble proteins, respectively, which makes the corresponding classification problem very unbalanced [39]. Those interval of solubility, although they generate an unbalanced classification problem, are of the same length and they are placed at the extremes of the (normalized) solubility range.…”

“…Moreover, the process of folding is in strict competition with the aggregation process (low propensity of a molecule to be soluble), that is, with the tendency of establishing inter-molecular bonds. This results in the formation of large multi-molecular aggregates which, analogously to what happens for artificial polymers, are insoluble and hence precipitate in solution [9,11,13].The herein considered data as been already processed by different groups [1,39,41]. Therefore, we use our previous results [39] for comparison in the herein presented experiments.…”

“…Toward this end, here we evaluate the ODSE classifier for sequences on an important application concerning the solubility analysis of sequences of amino acid residues, elaborated from the Escherichia coli (E. coli) proteome [31,32]. We contrast our experimental results with two reference systems and with respect to another recently-proposed classifier for sequences [39]. Folding is a chemico-physical process of extraordinary difficulty and complexity from the viewpoint of prediction.…”

“…The herein considered data as been already processed by different groups [1,39,41]. Therefore, we use our previous results [39] for comparison in the herein presented experiments. This paper is structured as follows.…”