Designing Labeled Graph Classifiers by Exploiting the Rényi Entropy of the Dissimilarity Representation

Livi, Lorenzo

doi:10.3390/e19050216

Cited by 4 publications

(2 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An edge e ij connecting x i and x j is weighted using a weight based on their distance, |e ij | = d 2 (x i , x j ). The α-order Rényi entropy (1) can be estimated according to a geometric interpretation of an entropic spanning graph of G. Examples of such graphs used in the literature are the MST, k -NN graph, Steiner tree, and TSP graph [9,14,25,36,42,[44][45][46]57]. In this paper, we will focus on the MST [8,44].…”

Section: Graph-based Entropy Estimationmentioning

confidence: 99%

Entropic One-Class Classifiers

Livi

Sadeghian

Pedrycz

2015

IEEE Trans. Neural Netw. Learning Syst.

Self Cite

View full text Add to dashboard Cite

The one-class classification problem is a well-known research endeavor in pattern recognition. The problem is also known under different names, such as outlier and novelty/anomaly detection. The core of the problem consists in modeling and recognizing patterns belonging only to a so-called target class. All other patterns are termed non-target, and therefore they should be recognized as such. In this paper, we propose a novel one-class classification system that is based on an interplay of different techniques. Primarily, we follow a dissimilarity representation based approach; we embed the input data into the dissimilarity space by means of an appropriate parametric dissimilarity measure. This step allows us to process virtually any type of data. The dissimilarity vectors are then represented through a weighted Euclidean graphs, which we use to (i) determine the entropy of the data distribution in the dissimilarity space, and at the same time (ii) derive effective decision regions that are modeled as clusters of vertices. Since the dissimilarity measure for the input data is parametric, we optimize its parameters by means of a global optimization scheme, which considers both mesoscopic and structural characteristics of the data represented through the graphs. The proposed one-class classifier is designed to provide both hard (Boolean) and soft decisions about the recognition of test patterns, allowing an accurate description of the classification process. We evaluate the performance of the system on different benchmarking datasets, containing either feature-based or structured patterns. Experimental results demonstrate the effectiveness of the proposed technique.

show abstract

Section: Graph-based Entropy Estimationmentioning

confidence: 99%

Entropic One-Class Classifiers

Livi

Sadeghian

Pedrycz

2015

IEEE Trans. Neural Netw. Learning Syst.

Self Cite

View full text Add to dashboard Cite

show abstract

“…In current implementations [20,24,25], the ODSE model is optimized through a genetic algorithm (GA). The GA operates by performing roulette wheel selection, two-point crossover, and random mutation on the variables representing the model parameters; it implements also an elitism strategy that includes the fittest individual into the next population.…”

Section: A Quick Look Into the Odse Designmentioning

confidence: 99%

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

Livi

Rizzi

Sadeghian

2015

IFS

Self Cite

View full text Add to dashboard Cite

We evaluate a version of the recently-proposed classification system named Optimized Dissimilarity Space Embedding (ODSE) that operates in the input space of sequences of generic objects. The ODSE system has been originally presented as a classification system for patterns represented as labeled graphs. However, since ODSE is founded on the dissimilarity space representation of the input data, the classifier can be easily adapted to any input domain where it is possible to define a meaningful dissimilarity measure. Here we demonstrate the effectiveness of the ODSE classifier for sequences by considering an application dealing with the recognition of the solubility degree of the Escherichia coli proteome. Solubility, or analogously aggregation propensity, is an important property of protein molecules, which is intimately related to the mechanisms underlying the chemico-physical process of folding. Each protein of our dataset is initially associated with a solubility degree and it is represented as a sequence of symbols, denoting the 20 amino acid residues. The herein obtained computational results, which we stress that have been achieved with no context-dependent tuning of the ODSE system, confirm the validity and generality of the ODSE-based approach for structured data classification

show abstract

Exploiting second-order dissimilarity representations for hierarchical clustering and visualization

Aidos

2022

Data Min Knowl Disc

View full text Add to dashboard Cite

Designing Labeled Graph Classifiers by Exploiting the Rényi Entropy of the Dissimilarity Representation

Cited by 4 publications

References 54 publications

Entropic One-Class Classifiers

Entropic One-Class Classifiers

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

Exploiting second-order dissimilarity representations for hierarchical clustering and visualization

Contact Info

Product

Resources

About