A decision theoretic rough fuzzy c-means algorithm
01 Nov 2015-pp 192-196
TL;DR: A clustering algorithm using DTRS and fuzzy sets in view called the decision-theoretic rough fuzzy c-means (DTRFCM) is developed and experiments show that this approach is more efficient than the D TRS algorithm.
Abstract: Imprecision based data clustering algorithms have gained a lot of importance these days because of the imprecise character of modern day databases. Some such algorithms are the rough c-means (RCM), fuzzy c-means (FCM) and their hybrid versions. Li et al used the decision theoretic rough set (DTRS) model by the way improving the RCM. In their approach they have used a notion called loss function to limit the information lost due to neighbours. The method of allocation using decision-theoretic rough sets model deals with potentially high computational cost. It has been observed that hybrid models are better than individual models. Keeping this in view, here we develop a clustering algorithm using DTRS and fuzzy sets in view called the decision-theoretic rough fuzzy c-means (DTRFCM). Experiments carried out show that our approach is more efficient than the DTRS algorithm. For this purpose we used several well-known data sets and parameters like the DB-index, D-index and Accuracy measure.
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01 Jan 1979
TL;DR: This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis and addressing interesting real-world computer Vision and multimedia applications.
Abstract: In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to: • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information. Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.
1,758 citations
01 Aug 2017
TL;DR: This paper analyses various ensemble methods (Bagged Tree, Random Forest, and AdaBoost) along with Feature subset selection method — Particle Swarm Optimization (PSO) to accurately predict the occurrence of heart disease for a particular patient.
Abstract: Advancement and emergence of newer technologies such as analytics, artificial intelligence, machine learning have impacted many sectors such as health care, automotive etc. In the healthcare sector, these technologies resulted in various benefits such as clinical decision support, better care coordination, improving patient wellness etc. The World over, Coronary Heart Disease (CHD) is affecting millions of people. Various machine learning techniques include ensemble classifiers can be used in healthcare for improving prediction accuracy. This paper analyses various ensemble methods (Bagged Tree, Random Forest, and AdaBoost) along with Feature subset selection method — Particle Swarm Optimization (PSO), to accurately predict the occurrence of heart disease for a particular patient. Experimental results show that Bagged Tree and PSO achieved the highest accuracy.
56 citations
Cites methods from "A decision theoretic rough fuzzy c-..."
...Resht Agrawal[17], proposed an approach for prediction of heart disease by using clustering algorithm (DTRS) and fuzzy set....
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...[17] Sresht Agrawal and B.K.Tripathy,” A Decision Theoretic Rough Fuzzy c-means Algorithm”,IEEE (ICRCICN) 2015,pp.192-196....
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06 Nov 2020
TL;DR: In this article, the authors proposed a model for the application of GA in diagnosing disease and predicting accuracy, which demonstrated that the amalgamation of a small subset of input features produces the optimum performance than the use of all the single significant features individually.
Abstract: Earlier risk assessment and identification of different diseases is the most crucial issue for avoiding and lowering their progression. The researchers typically used the statistical comparative analysis or step-by-step methods of feature selection using regression techniques to estimate the risk factors of diseases. The outcomes from these methods emphasized on individual risk factors separately. A combination of factors, however, is likely to affect the development of disease rather than just anyone alone. Genetic algorithms (GA) can be useful and efficient for searching a combination of factors for the fast diagnosis with best accuracies, especially for a large number of complex and poorly understood factors, as in the case in the prediction of disease development. Our proposed model shows the potential for the application of GA in diagnosing disease and predicting accuracy. Our proposed model demonstrated that the amalgamation of a small subset of input features produces the optimum performance than the use of all the single significant features individually. This model not only predicts the best feature sets and accuracy but also overcome the problem of missing values present in the dataset. Variables more frequently selected by LR might be more important for the prediction of disease development and accuracies by GA.
3 citations
24 Mar 2017
TL;DR: A rough kernelized fuzzy c-means clustering (RKFCM) based medical image segmentation algorithm that uses rough set with KFCM for removal of uncertainty by introduction of higher and lower estimation of rough set theory.
Abstract: This paper presents a rough kernelized fuzzy c-means clustering (RKFCM) based medical image segmentation algorithm. It is a combination of rough set and kernelized FCM clustering (KFCM). KFCM introduced new technique of clustering using kernel induced distance and improved its robustness towards noise. However, it is failed to remove the vagueness and uncertainty of the clustering technique. In this paper, we use rough set with KFCM for removal of uncertainty by introduction of higher and lower estimation of rough set theory. The objective function derived from KFCM is merged with rough set to get better segmentation results. Experiments performed on numerous medical image data sets and its resulting validity index values have proved this algorithm to be more efficient in comparison to existing algorithms.
3 citations
01 Jan 2017
TL;DR: This work has selected three of the most popular kernels and developed an improved Kernelized rough c-means algorithm that can be used for data clustering and compares the results with the basic decision theoretic rough c -means.
Abstract: There are several algorithms used for data clustering and as imprecision has become an inherent part of datasets now days, many such algorithms have been developed so far using fuzzy sets, rough sets, intuitionistic fuzzy sets, and their hybrid models. In order to increase the flexibility of conventional rough approximations, a probability based rough sets concept was introduced in the 90s namely decision theoretic rough sets (DTRS). Using this model Li et al. extended the conventional rough c-means. Euclidean distance has been used to measure the similarity among data. As has been observed the Euclidean distance has the property of separability. So, as a solution to that several Kernel distances are used in literature. In fact, we have selected three of the most popular kernels and developed an improved Kernelized rough c-means algorithm. We compare the results with the basic decision theoretic rough c-means. For the comparison we have used three datasets namely Iris, Wine and Glass. The three Kernel functions used are the Radial Basis, the Gaussian, and the hyperbolic tangent. The experimental analysis by using the measuring indices DB and D show improved results for the Kernelized means. We also present various graphs to showcase the clustered data.
2 citations
Cites methods from "A decision theoretic rough fuzzy c-..."
...The decision theoretic rough c-means (DTRCM) was introduced recently and has been extended to the context of fuzzy setting in [1] and intuitionistic fuzzy setting in [2]....
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References
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01 Jan 1998
12,940 citations
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...Assign initial means vI for c clusters....
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Book•
31 Oct 1991
TL;DR: Theoretical Foundations.
Abstract: I. Theoretical Foundations.- 1. Knowledge.- 1.1. Introduction.- 1.2. Knowledge and Classification.- 1.3. Knowledge Base.- 1.4. Equivalence, Generalization and Specialization of Knowledge.- Summary.- Exercises.- References.- 2. Imprecise Categories, Approximations and Rough Sets.- 2.1. Introduction.- 2.2. Rough Sets.- 2.3. Approximations of Set.- 2.4. Properties of Approximations.- 2.5. Approximations and Membership Relation.- 2.6. Numerical Characterization of Imprecision.- 2.7. Topological Characterization of Imprecision.- 2.8. Approximation of Classifications.- 2.9. Rough Equality of Sets.- 2.10. Rough Inclusion of Sets.- Summary.- Exercises.- References.- 3. Reduction of Knowledge.- 3.1. Introduction.- 3.2. Reduct and Core of Knowledge.- 3.3. Relative Reduct and Relative Core of Knowledge.- 3.4. Reduction of Categories.- 3.5. Relative Reduct and Core of Categories.- Summary.- Exercises.- References.- 4. Dependencies in Knowledge Base.- 4.1. Introduction.- 4.2. Dependency of Knowledge.- 4.3. Partial Dependency of Knowledge.- Summary.- Exercises.- References.- 5. Knowledge Representation.- 5.1. Introduction.- 5.2. Examples.- 5.3. Formal Definition.- 5.4. Significance of Attributes.- 5.5. Discernibility Matrix.- Summary.- Exercises.- References.- 6. Decision Tables.- 6.1. Introduction.- 6.2. Formal Definition and Some Properties.- 6.3. Simplification of Decision Tables.- Summary.- Exercises.- References.- 7. Reasoning about Knowledge.- 7.1. Introduction.- 7.2. Language of Decision Logic.- 7.3. Semantics of Decision Logic Language.- 7.4. Deduction in Decision Logic.- 7.5. Normal Forms.- 7.6. Decision Rules and Decision Algorithms.- 7.7. Truth and Indiscernibility.- 7.8. Dependency of Attributes.- 7.9. Reduction of Consistent Algorithms.- 7.10. Reduction of Inconsistent Algorithms.- 7.11. Reduction of Decision Rules.- 7.12. Minimization of Decision Algorithms.- Summary.- Exercises.- References.- II. Applications.- 8. Decision Making.- 8.1. Introduction.- 8.2. Optician's Decisions Table.- 8.3. Simplification of Decision Table.- 8.4. Decision Algorithm.- 8.5. The Case of Incomplete Information.- Summary.- Exercises.- References.- 9. Data Analysis.- 9.1. Introduction.- 9.2. Decision Table as Protocol of Observations.- 9.3. Derivation of Control Algorithms from Observation.- 9.4. Another Approach.- 9.5. The Case of Inconsistent Data.- Summary.- Exercises.- References.- 10. Dissimilarity Analysis.- 10.1. Introduction.- 10.2. The Middle East Situation.- 10.3. Beauty Contest.- 10.4. Pattern Recognition.- 10.5. Buying a Car.- Summary.- Exercises.- References.- 11. Switching Circuits.- 11.1. Introduction.- 11.2. Minimization of Partially Defined Switching Functions.- 11.3. Multiple-Output Switching Functions.- Summary.- Exercises.- References.- 12. Machine Learning.- 12.1. Introduction.- 12.2. Learning From Examples.- 12.3. The Case of an Imperfect Teacher.- 12.4. Inductive Learning.- Summary.- Exercises.- References.
7,826 citations
TL;DR: A measure is presented which indicates the similarity of clusters which are assumed to have a data density which is a decreasing function of distance from a vector characteristic of the cluster which can be used to infer the appropriateness of data partitions.
Abstract: A measure is presented which indicates the similarity of clusters which are assumed to have a data density which is a decreasing function of distance from a vector characteristic of the cluster. The measure can be used to infer the appropriateness of data partitions and can therefore be used to compare relative appropriateness of various divisions of the data. The measure does not depend on either the number of clusters analyzed nor the method of partitioning of the data and can be used to guide a cluster seeking algorithm.
6,757 citations
01 Jan 1973
TL;DR: Two fuzzy versions of the k-means optimal, least squared error partitioning problem are formulated for finite subsets X of a general inner product space; in both cases, the extremizing solutions are shown to be fixed points of a certain operator T on the class of fuzzy, k-partitions of X, and simple iteration of T provides an algorithm which has the descent property relative to the least squarederror criterion function.
Abstract: Two fuzzy versions of the k-means optimal, least squared error partitioning problem are formulated for finite subsets X of a general inner product space. In both cases, the extremizing solutions are shown to be fixed points of a certain operator T on the class of fuzzy, k-partitions of X, and simple iteration of T provides an algorithm which has the descent property relative to the least squared error criterion function. In the first case, the range of T consists largely of ordinary (i.e. non-fuzzy) partitions of X and the associated iteration scheme is essentially the well known ISODATA process of Ball and Hall. However, in the second case, the range of T consists mainly of fuzzy partitions and the associated algorithm is new; when X consists of k compact well separated (CWS) clusters, Xi , this algorithm generates a limiting partition with membership functions which closely approximate the characteristic functions of the clusters Xi . However, when X is not the union of k CWS clusters, the limi...
5,787 citations
01 Jan 1973
TL;DR: In this paper, two fuzzy versions of the k-means optimal, least squared error partitioning problem are formulated for finite subsets X of a general inner product space, and the extremizing solutions are shown to be fixed points of a certain operator T on the class of fuzzy, k-partitions of X, and simple iteration of T provides an algorithm which has the descent property relative to the LSE criterion function.
Abstract: Two fuzzy versions of the k-means optimal, least squared error partitioning problem are formulated for finite subsets X of a general inner product space. In both cases, the extremizing solutions are shown to be fixed points of a certain operator T on the class of fuzzy, k-partitions of X, and simple iteration of T provides an algorithm which has the descent property relative to the least squared error criterion function. In the first case, the range of T consists largely of ordinary (i.e. non-fuzzy) partitions of X and the associated iteration scheme is essentially the well known ISODATA process of Ball and Hall. However, in the second case, the range of T consists mainly of fuzzy partitions and the associated algorithm is new; when X consists of k compact well separated (CWS) clusters, Xi , this algorithm generates a limiting partition with membership functions which closely approximate the characteristic functions of the clusters Xi . However, when X is not the union of k CWS clusters, the limi...
5,254 citations