Intrusion detection model using fusion of chi-square feature selection and multi class SVM

Three-way cognitive concept learning via multi-granularity

Incomplete decision contexts: Approximate concept construction, rule acquisition and knowledge reduction

The main task of granular computing (GrC) is about representing, constructing, and processing information granules. Information granules are formalized in many different approaches. Different formal approaches emphasize the same fundamental facet in different ways. In this paper, we propose a novel GrC method of machine learning by using formal concept description of information granules. Based on information granules, the model and mechanism of two-way learning system is constructed in fuzzy datasets. It is addressed about how to train arbitrary fuzzy information granules to become necessary, sufficient, and necessary and sufficient fuzzy information granules. Moreover, an algorithm of the presented approach is established, and the complexity of the algorithm is analyzed carefully. Finally, to interpret and help understand the theories and algorithm, a real-life case study is considered and experimental evaluation is performed by five datasets from the University of California—Irvine, which is valuable for applying these theories to deal with practical issues.

Granular Computing Approach to Two-Way Learning Based on Formal Concept Analysis in Fuzzy Datasets

박응격 저, 「行政學講義」(서울 : 박영사, 1993)

During the design of concept lattices, complexity plays a major role in computing all the concepts from the huge incidence matrix. Hence for reducing the size of the lattice, methods based on matrix decompositions like SVD are available in the literature. However, SVD computation is known to have large time and memory requirements. In this paper, we propose a new method based on Fuzzy K-Means clustering for reducing the size of the concept lattices. We demonstrate the implementation of proposed method on two application areas: information retrieval and information visualization.

Short Communication: Concept lattice reduction using fuzzy K-Means clustering

Formal Concept Analysis (FCA), in which data is represented as a formal context, offers a framework for Association Rules Mining (ARM) by handling functional dependencies in the data. However, with the size of the formal context, the number of rules grows exponentially. In this article, we apply Fuzzy K-Means clustering on the data set to reduce the formal context and FCA on the reduced data set for mining association rules. With experiments on two real-world healthcare data sets, we offer the evidence for performance of FKM-based FCA in mining association rules.

Fuzzy clustering-based formal concept analysis for association rules mining

Bipolar fuzzy graph representation of concept lattice

In recent times Formal Concept Analysis (FCA), in which the data is represented as a formal context, has gained popularity for Association Rules Mining (ARM). Application of ARM in health care datasets is challenging and a highly rewarding problem. However, datasets in the medical domain are of high dimension. As the dimensionality of dataset increases, size of the formal context as well as complexity of FCA based ARM also increases. To handle the problem of high dimensionality and mine the associations, we propose to apply Singular Value Decomposition (SVD) on the dataset to reduce the dimensionality and apply FCA on the reduced dataset for ARM. To demonstrate the proposed method, experiments are conducted on Tuberculosis (TB) and Hypertension (HP) datasets. Results indicate that with fewer concepts, SVD based FCA has achieved the performance of FCA on TB data and performed better than FCA on HP data.

Mining associations in health care data using formal concept analysis and singular value decomposition

Intrusion detection systems (IDS) play a major role in detecting the attacks that occur in the computer or networks. Anomaly intrusion detection models detect new attacks by observing the deviation from profile. However there are many problems in the traditional IDS such as high false alarm rate, low detection capability against new network attacks and insufficient analysis capacity. The use of machine learning for intrusion models automatically increases the performance with an improved experience. This paper proposes a novel method of integrating principal component analysis (PCA) and support vector machine (SVM) by optimizing the kernel parameters using automatic parameter selection technique. This technique reduces the training and testing time to identify intrusions thereby improving the accuracy. The proposed method was tested on KDD data set. The datasets were carefully divided into training and testing considering the minority attacks such as U2R and R2L to be present in the testing set to identify the occurrence of unknown attack. The results indicate that the proposed method is successful in identifying intrusions. The experimental results show that the classification accuracy of the proposed method outperforms other classification techniques using SVM as the classifier and other dimensionality reduction or feature selection techniques. Minimum resources are consumed as the classifier input requires reduced feature set and thereby minimizing training and testing overhead time.

Ch. Aswani Kumar

Papers

Short Communication: Concept lattice reduction using fuzzy K-Means clustering

Fuzzy clustering-based formal concept analysis for association rules mining

Bipolar fuzzy graph representation of concept lattice

Mining associations in health care data using formal concept analysis and singular value decomposition

Intrusion detection model using fusion of PCA and optimized SVM