VPMCD: Variable interaction modeling approach for class discrimination in biological systems

Abstract

Data classification algorithms applied for class prediction in computational biology literature are data specific and have shown varying degrees of performance. Different classes cannot be distinguished solely based on interclass distances or decision boundaries. We propose that inter-relations among the features be exploited for separating observations into specific classes. A new variable predictive model based class discrimination (VPMCD) method is described here. Three well established and proven data sets of varying statistical and biological significance are utilized as benchmark. The performance of the new method is compared with advanced classification algorithms. The new method performs better during different tests and shows higher stability and robustness. The VPMCD is observed to be a potentially strong classification approach and can be effectively extended to other data mining applications involving biological systems.

Rolling bearing fault diagnosis based on partially ensemble empirical mode decomposition and variable predictive model-based class discrimination

“…Based on this, the outputs of testing data of VPMCD multi-classifier as well as their identifying rate are given in Table 5. Comparing with Table 4, it is easy to find that the X 1 -X 10 1 1(10) 1(10) 1(10) 1(10) 1(10) 1(10) X 11 -X 20 2 2(10) 2(10) 2(10) 2(10) 2(10) 2(10) X 21 -X 30 3 3(6), 7(3), 4(1) 3(10) 3(10) 3(0), 4(10) 3(10) 3(7), 4(3) X 31 -X 40 4 4(10) 4(10) 4(10) 4(10) 4(10) 4(10) X 41 -X 50 5 5(10) 5(10) 5(10) 5(10) 5(10) 5(10) X 51 -X 60 6 6(10) 6(10) 6(9), 7(1) 6(5), 7(5) 6(9), 7(1) 6(9), 7(1) X 61 -X 70 7 7(9), 6(1) 7(8), 6(1), 2(1) 7(10) 7(9), 6(1) 7 X 1 -X 10 1 1(10) 1(10) 1(10) 1(10) 1(10) 1(10) X 11 -X 20 2 2(10) 2(10) 2(10) 2(10) 2(10) 2(10) X 21 -X 30 3 3(10) 3(10) 3(10) 3(10) 3(10) 3(10) X 31 -X 40 4 4(10) 4(10) 4(10) 4(10) 4(10) 4(10) X 41 -X 50 5 5(10) 5(10) 5(10) 5(10) 5(10) 5(10) X 51 -X 60 6 6(10) 6(10) 6(10) 6(10) 6(8), 7(2) 6(9), 7(1) X 61 -X 70 7 7(8), 6(2) a 7 ( a r c h i v e s o f c i v i l a n d m e c h a n i c a l e n g i n e e r i n g 1 6 ( 2 0 1 6 ) 7 8 4 -7 9 4 identification rates of testing data without sorting by LS are all lower than that of features optimized by LS for all J (from 2 to 7). It indicates that the feature selection by using LS is essential and dominant.…”

“…However, the proposed method also has some problems including the number selection of features and the self-adaptivity of adding white noise in PEEMD that will be studied in the future work. X 1 -X 10 1 1(10) 1(10) 1(10) 1(10) X 11 -X 20 2 2(9), 3(1) 2(10) 2(10) 2(10) X 21 -X 30 3 3(10) 3(10) 3(10) 3(10) X 31 -X 40 4 4(10) 4(9), 5(1) 4(10) 4(10) X 41 -X 50 5 5(10) 5(10) 5(10) 5(9), 4(1) X 51 -X 60 6 6(10) 6(10) 6(10) 6(10) X 61 -X 70 7 7(8), 6(2) 7(10) 7 (10) BPNN (J = 6) X 1 -X 10 1 1(10) 1(10) 1(10) 1(10) X 11 -X 20 2 2(10) 2(10) 2(10) 2(10) X 21 -X 30 3 3(10) 3(10) 3(10) 3(10) X 31 -X 40 4 4(10) 4(10) 4(10) 4(10) X 41 -X 50 5 5(10) 5(10) 5(10) 5(10) X 51 -X 60 6 6(10) 6(10) 6(10) 6(10) X 61 -X 70 7 7(8), 6(2) 7(4), 6(6) 7(9), 6(1) 7 (8) a r c h i v e s o f c i v i l a n d m e c h a n i c a l e n g i n e e r i n g 1 6 ( 2 0 1 6 ) 7 8 4 -7 9 4…”

“…After obtaining the features, a classification method is needed for an automatic fault diagnosis. Variable predictive model-based class discriminate (VPMCD) introduced by Raghuraj and Lakshminarayanan [29,30] is based on the inter-relations (linear or nonlinear) among the feature values to construct the variable predictive model and has been proved an efficient supervised learning algorithm. In this paper the VPMCD method is employed to build a multi-classifier for identifying the fault categories and severities automatically.…”

“…ANFIS also has the similar problems to ANN which is a time-consuming training especially for a high dimension of feature. Recently, the variable predictive model-based class discrimination (VPMCD) was proposed by Raghuraj and Lakshminarayanan [29,30] for biological systems and protein structural classification. In VPMCD method, mathematical models for each feature elements are firstly established based on the inherent relationships (like linear or quadric) between the eigenvalues.…”

A novel feature selection method to boost variable predictive model–based class discrimination performance and its application to intelligent multi-fault diagnosis

“…Based on this hypothesis, a new multiclass discrimination method, called variable prediction model class discrimination (VPMCD), has been proposed and applied to medicine signal analysis. [26][27][28] The VPMCD can establish mathematical variable prediction models (VPMs) to discover the intrinsic and quantitative interactions among these feature variables and utilize these VPMs to identify the classes of unknown test samples. The VPMCD is implemented in the following two steps described as Figure 1.…”