4473346
Multi-objective hierarchical genetic algorithm for interpretable fuzzy rule-based knowledge extraction
A new scheme based on multi-objective hierarchical genetic algorithm (MOHGA) is proposed to extract interpretable rule-based knowledge from data. The approach is derived from the use of multiple objective genetic algorithm (MOGA), where the genes of the chromosome are arranged into control genes and parameter genes. These genes are in a hierarchical form so that the control genes can manipulate the parameter genes in a more effective manner. The effectiveness of this chromosome formulation enables the fuzzy sets and rules to be optimally reduced. Some important concepts about the interpretability are introduced and the fitness function in the MOGA will consider both the accuracy and interpretability of the fuzzy model. In order to remove the redundancy of the rule base proactively, we further apply an interpretability-driven simplification method to newborn individuals. In our approach, we first apply the fuzzy clustering to generate an initial rule-based model. Then the multi-objective hierarchical genetic algorithm and the recursive least square method are used to obtain the optimized fuzzy models. The accuracy and the interpretability of fuzzy models derived by this approach are studied and presented in this paper. We compare our work with other methods reported in the literature on four examples: a synthetic nonlinear dynamic system, a nonlinear static system, the Lorenz system and the Mackey-Glass system. Simulation results show that the proposed approach is effective and practical in knowledge extraction.
15118493
Anomaly intrusion detection using multi-objective genetic fuzzy system and agent-based evolutionary computation framework
In this paper, we present a multi-objective genetic fuzzy system for anomaly intrusion detection. The proposed system extracts accurate and interpret able fuzzy rule-based knowledge from network data using an agent-based evolutionary computation framework. The experimental results on KDD-Cup99 intrusion detection benchmark data demonstrate that our system can achieve high detection rate for intrusion attacks and low false positive rate for normal network traffic.
14060305
Effectively Detecting All-Zero DCT Blocks for H.264 Optimization
This paper presents a novel efficient algorithm to reduce redundant DCT and quantization computations for H.264 encoding. A theoretical analysis is performed to study the sufficient condition for DCT coefficients to be quantized to zeros in H.264. As a result, a more tight sufficient condition is derived to early detect all-zero 4x4 DCT blocks. Compared with other algorithms, the proposed algorithm provides a more precise and efficient condition to predict all-zero DCT blocks. The experimental results demonstrate that the proposed algorithm outperforms other algorithms in terms of all the evaluated performances.
2060937
Efficient prediction algorithm of integer DCT coefficients for H.264/AVC optimization
This paper presents a novel efficient prediction algorithm to reduce redundant discrete cosine transform (DCT) and quantization computations for H.264 encoding optimization. A theoretical analysis is performed to study the sufficient condition for DCT coefficients to be quantized to zeros. As a result, three sufficient conditions corresponding to three types of transform and quantization methods in H.264 are proposed. Compared with other algorithms in the literature, the proposed algorithm derives more precise and efficient conditions to predict zero quantized DCT coefficients. Both the theoretical analysis and experimental results demonstrate that the proposed algorithm is superior to other algorithms in terms of the computational complexity reduction, encoded video quality, false acceptance rate, and false rejection rate.
3107591
Agent-based evolutionary approach for interpretable rule-based knowledge extraction
An agent-based evolutionary approach is proposed to extract interpretable rule-based knowledge. In the multiagent system, each fuzzy set agent autonomously determines its own fuzzy sets information, such as the number and distribution of the fuzzy sets. It can further consider the interpretability of fuzzy systems with the aid of hierarchical chromosome formulation and interpretability-based regulation method. Based on the obtained fuzzy sets, the Pittsburgh-style approach is applied to extract fuzzy rules that take both the accuracy and interpretability of fuzzy systems into consideration. In addition, the fuzzy set agents can cooperate with each other to exchange their fuzzy sets information and generate offspring agents. The parent agents and their offspring compete with each other through the arbitrator agent based on the criteria associated with the accuracy and interpretability to allow them to remain competitive enough to move into the next population. The performance with emphasis upon both the accuracy and interpretability based on the agent-based evolutionary approach is studied through some benchmark problems reported in the literature. Simulation results show that the proposed approach can achieve a good tradeoff between the accuracy and interpretability of fuzzy systems.
1881554
Optimization of Gaussian Mixture Model Parameters for Speaker Identification
Gaussian mixture model (GMM) [1] has been widely used for modeling speakers. In speaker identification, one major problem is how to generate a set of GMMs for identification purposes based upon the training data. Due to the hill-climbing characteristic of the maximum likelihood (ML) method, any arbitrary estimate of the initial model parameters will usually lead to a sub-optimal model in practice. To resolve this problem, this paper proposes a hybrid training method based on the genetic algorithm (GA). It utilizes the global searching capability of the GA and combines the effectiveness of the ML method.
28149553
Fingerprint Indexing Based On Symmetrical Measurement
Due to the small number of classes and uneven distribution, the Henry's fingerprint classification approach is not able to provide an efficient way to narrow down the search when identifying a person in a large database. In this paper, three symmetrical filters are used to measure different orientation structures. We proposed a new fingerprint indexing approach based on these three symmetrical measurements. Experiments conducted on the NIST database 4 show the effectiveness of the proposed approach
7895439
An efficient rate-distortion optimal shape coding approach utilizing a skeleton-based decomposition
In this paper, we present a new shape-coding approach, which decouples the shape information into two independent signal data sets; the skeleton and the boundary distance from the skeleton. The major benefit of this approach is that it allows for a more flexible tradeoff between approximation error and bit budget. Curves of arbitrary order can be utilized for approximating both the skeleton and distance signals. For a given bit budget for a video frame, we solve the problem of choosing the number and location of the control points for all skeleton and distance signals of all boundaries within a frame, so that the overall distortion is minimized. An operational rate-distortion (ORD) optimal approach using Lagrangian relaxation and a four-dimensional direct acyclic graph (DAG) shortest path algorithm is developed for solving the problem. To reduce the computational complexity from O(N(5)) to O(N(3)), where N is the number of admissible control points for a skeleton, a suboptimal greedy-trellis search algorithm is proposed and compared with the optimal algorithm. In addition, an even more efficient algorithm with computational complexity O(N(2)) that finds an ORD optimal solution using a relaxed distortion criterion is also proposed and compared with the optimal solution. Experimental results demonstrate that our proposed approaches outperform existing ORD optimal approaches, which do not follow the same decomposition of the source data.
14482795
Collaborative multi-agents for workflow management
Workflow management helps organizations to organize their work on different activities in such a way that the predictable underlying processes are carried out effectively and efficiently in a distributed environment. We describe a business workflow management system with intelligent multi-agent support in a collaborative organization environment. With the help from multi-agent collaboration, our workflow management system can organize the organization's work processes and provide resource control, data mediation, semantic mapping, and knowledge visualization processing. Such systems are able to manage the organizational workflow more effectively and more efficiently.
16354227
Using symmetry in robust model fitting
The pattern recognition and computer vision communities often employ robust methods for model fitting. In particular, high breakdown-point methods such as least median of squares (LMedS) and least trimmed squares (LTS) have often been used in situations where the data are contaminated with outliers. However, though the breakdown point of these methods can be as high as 50% (they can be robust to up to 50% contamination), they can break down at unexpectedly lower percentages when the outliers are clustered. In this paper, we demonstrate the fragility of LMedS and LTS and analyze the reasons that cause the fragility of these methods in the situation when a large percentage of clustered outliers exist in the data. We adapt the concept of "symmetry distance" to formulate an improved regression method, called the least trimmed symmetry distance (LTSD). Experimental results are presented to show that the LTSD performs better than LMedS and LTS under a large percentage of clustered outliers and large standard variance of inliers.
10547222
A Hybrid Ontology Directed Feedback Selection Algorithm for Supporting Creative Problem Solving Dialogues
We evaluate a new hybrid language processing approach designed for interactive applications that maintain an interaction with users over multiple turns. Specifically, we describe a method for using a simple topic hierarchy in combination with a standard information retrieval measure of semantic similarity to reason about the selection of appropriate feedback in response to extended language inputs in the context of an interactive tutorial system designed to support creative problem solving. Our evaluation demonstrates the value of using a machine learning approach that takes feedback from experts into account for optimizing the hierarchy based feedback selection strategy.