One-class classifiers based on entropic spanning graphs

One-class classifiers offer valuable tools to assess the presence of outliers in data. In this paper, we propose a design methodology for one-class classifiers based on entropic spanning graphs. Our approach takes into account the possibility to process also non-numeric data by means of an embedding procedure. The spanning graph is learned on the embedded input data and the outcoming partition of vertices defines the classifier. The final partition is derived by exploiting a criterion based on mutual information minimization. Here, we compute the mutual information by using a convenient formulation provided in terms of the $\alpha$-Jensen difference. Once training is completed, in order to associate a confidence level with the classifier decision, a graph-based fuzzy model is constructed. The fuzzification process is based only on topological information of the vertices of the entropic spanning graph. As such, the proposed one-class classifier is suitable also for data characterized by complex geometric structures. We provide experiments on well-known benchmarks containing both feature vectors and labeled graphs. In addition, we apply the method to the protein solubility recognition problem by considering several representations for the input samples. Experimental results demonstrate the effectiveness and versatility of the proposed method with respect to other state-of-the-art approaches.Comment: Extended and revised version of the paper "One-Class Classification Through Mutual Information Minimization" presented at the 2016 IEEE IJCNN, Vancouver, Canad

Fuzzy expert systems in civil engineering

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AN INTERVAL TYPE 2 FUZZY EVIDENTIAL REASONING APPROACH TO PERSONNEL RECRUITMENT

Recruitment process is a procedure of selecting an ideal candidate amongst different applicants who suit the qualifications required by the given institution in the best way. Due to the multi criteria nature of the recruitment process, it involves contradictory, numerous and incommensurable criteria that are based on quantitative and qualitative measurements. Quantitative criteria evaluation are not always dependent on the judgement of the expert, they are expressed in either monetary terms or engineering measurements, meanwhile qualitative criteria evaluation depend on the subjective judgement of the decision maker, human evaluation which is often characterized with subjectivity and uncertainties in decision making. Given the uncertain, ambiguous, and vague nature of recruitment process there is need for an applicable methodology that could resolve various inherent uncertainties of human evaluation during the decision making process. This work thus proposes an interval type 2 fuzzy evidential reasoning approach to recruitment process. The approach is in three phases; in the first phase in order to capture word uncertainty an interval type 2(IT2) fuzzy set Hao and Mendel Approach (HMA) is proposed to model the qualification requirement for recruitment process. This approach will cater for both intra and inter uncertainty in decision makers’judgments and demonstrates agreements by all subjects (decision makers) for the regular overlap of subject data intervals and the manner in which data intervals are collectively classified into their respective footprint of uncertainty. In the second phase the Intervaltype 2 fuzzy Analytical hierarchical process was employed as the weighting model to determine the weight of each criterion gotten from the decision makers. In the third phase the interval type 2 fuzzy was hybridized with the ranking evidential reasoning algorithm to evaluate each applicant to determine their final score in order to choose the most ideal candidate for recruitment.The implementation tool for phase two and three is Java programming language. Application of this proposed approach in recruitment process will resolve both intra and inter uncertainty in decision maker’s judgement and give room for consistent ranking even in place of incomplete requirement

Interval-valued analysis for discriminative gene selection and tissue sample classification using microarray data

AbstractAn important application of gene expression data is to classify samples in a variety of diagnostic fields. However, high dimensionality and a small number of noisy samples pose significant challenges to existing classification methods. Focused on the problems of overfitting and sensitivity to noise of the dataset in the classification of microarray data, we propose an interval-valued analysis method based on a rough set technique to select discriminative genes and to use these genes to classify tissue samples of microarray data. We first select a small subset of genes based on interval-valued rough set by considering the preference-ordered domains of the gene expression data, and then classify test samples into certain classes with a term of similar degree. Experiments show that the proposed method is able to reach high prediction accuracies with a small number of selected genes and its performance is robust to noise