LCD 패널 상의 불량 분류를 위한 Laplacian 그래프를 이용한 특성 추출 방법

학위논문 (석사)-- 서울대학교 대학원 : 컴퓨터공학과, 2012. 8. 유석인.LCD 패널 위에 존재하는 불량은 크게 4가지 종류로 나눌 수 있다. 각 불량은 서로 다른 방식으로 다뤄야 하기 때문에 정확한 분류방법이 필요하다. 정확한 분류를 위해서는 불량은 잘 나타내는 특성들과 좋은 분류기를 사용해야 한다. 이 논문에서는 불량을 표현하기 위해 불량 영역의 밝기, 모양, 통계적인 특성들을 사용하고, 가우시안 혼합 모델을 이용한 베이즈 분류방법을 사용하여 불량을 분류하게 된다. 하지만 불량을 나타내는 특성 중에 노이즈가 존재하거나 분류에 관련이 없는 특성들이 많이 존재하는 경우에는 분류 결과가 안 좋게 나올 수 있다. 따라서 여기서는 특성 추출방법을 사용하게 된다. 특성 추출 방법은 연관성이 적은 특성들이 분류에 미치는 영향을 줄여줄 뿐만 아니라 데이터의 차원을 줄여주어 분석을 용이하게 해주면서 계산 속도를 향상시키는 효과를 낼 수 있다. 주요 성분 분석 방법은 이러한 특성 추출 방법의 중 가장 유명한 방법중의 하나로 좋은 성능을 낸다고 알려져 있다. 하지만 주요 성분 분석 방법 역시 많은 수의 의미 없는 특성들이 존재하는 경우에는 나쁜 성능을 보여준다. 이러한 문제를 해결하기 위해 이 논문에서는 스펙트럴 그래프 이론을 이용한 특성 추출 방법을 제안하였다. 주요 성분 분석 방법이 데이터의 공분산 행렬을 사용하는 것과는 달리, 제안된 방법은 샘플 간의 유사도를 기반으로 한 그래프 라플라시안 매트릭스를 생성하여 그 고유 값과 고유 벡터를 사용하는 방법이다. 실험 결과를 보면 알 수 있듯이 그래프 라플라시안을 이용한 특성 추출 방법은 주요 성분 분석을 사용한 경우보다 더 좋은 분류 성공률을 보여준다. 또한 제안한 방법이 임의로 의미 없는 특성이 추가된 경우의 실험에 대해서도 매우 꾸준한 성능을 보여줌을 알 수 있다.There are four types of defects on LCD panel. For exact classification for the defects, good feature selection and classifier are necessary. In this paper, various features such as brightness, shape and statistical features are stated and Bayes classifier using Gaussian mixture model is used as classifier. But noisy or irrelevant features can harass the classification result. Feature extraction method can reduce the influence of irrelevant features and dimensionality to analyze complicated data well. Principal Component Analysis was one of the most famous feature extraction method had appropriate performance. However PCA would produce poor result if many noisy features exist. To solve that problem of PCA, feature extraction method based on spectral graph theory is proposed. Unlike PCA, proposed method using graph Laplacian matrix based on similarity instead of covariance matrix for analyzing spectral system. Experimental result shows that feature extraction method using graph Laplacian produces better performance than the result using PCA. And also proposed method is very robust to randomly added noisy features.Chapter 1. Introduction 1 Chapter 2. Defects and features 4 2.1. Definition 4 2.2. Brightness features 6 2.3. Shape features 6 2.4. Statistical features 9 Chapter 3. Feature extraction 11 3.1. Principal component analysis 11 3.2. Graph Laplacian 12 Chapter 4. Gaussian mixture models 14 4.1. Training 14 4.2. Classification 17 Chapter 5. Experiment 18 Chapter 6. Conclusion 21 ReferenceMaste

Simultaneous Bayesian Clustering and Feature Selection Through Student’s t{t} Mixtures Model

In this paper, we proposed a generative model for feature selection under the unsupervised learning context. The model assumes that data are independently and identically sampled from a finite mixture of Student?s t distributions, which can reduce the sensitiveness to outliers. Latent random variables that represent the features? salience are included in the model for the indication of the relevance of features. As a result, the model is expected to simultaneously realize clustering, feature selection, and outlier detection. Inference is carried out by a tree-structured variational Bayes algorithm. Full Bayesian treatment is adopted in the model to realize automatic model selection. Controlled experimental studies showed that the developed model is capable of modeling the data set with outliers accurately. Further- more, experiment results showed that the developed algorithm compares favorably against existing unsupervised probability model-based Bayesian feature selection algorithms on artificial and real data sets. Moreover, the application of the developed algorithm on real leukemia gene expression data indicated that it is able to identify the discriminating genes successfully

Aco-based feature selection algorithm for classification

Dataset with a small number of records but big number of attributes represents a phenomenon called “curse of dimensionality”. The classification of this type of dataset requires Feature Selection (FS) methods for the extraction of useful information. The modified graph clustering ant colony optimisation (MGCACO) algorithm is an effective FS method that was developed based on grouping the highly correlated features. However, the MGCACO algorithm has three main drawbacks in producing a features subset because of its clustering method, parameter sensitivity, and the final subset determination. An enhanced graph clustering ant colony optimisation (EGCACO) algorithm is proposed to solve the three (3) MGCACO algorithm problems. The proposed improvement includes: (i) an ACO feature clustering method to obtain clusters of highly correlated features; (ii) an adaptive selection technique for subset construction from the clusters of features; and (iii) a genetic-based method for producing the final subset of features. The ACO feature clustering method utilises the ability of various mechanisms such as intensification and diversification for local and global optimisation to provide highly correlated features. The adaptive technique for ant selection enables the parameter to adaptively change based on the feedback of the search space. The genetic method determines the final subset, automatically, based on the crossover and subset quality calculation. The performance of the proposed algorithm was evaluated on 18 benchmark datasets from the University California Irvine (UCI) repository and nine (9) deoxyribonucleic acid (DNA) microarray datasets against 15 benchmark metaheuristic algorithms. The experimental results of the EGCACO algorithm on the UCI dataset are superior to other benchmark optimisation algorithms in terms of the number of selected features for 16 out of the 18 UCI datasets (88.89%) and the best in eight (8) (44.47%) of the datasets for classification accuracy. Further, experiments on the nine (9) DNA microarray datasets showed that the EGCACO algorithm is superior than the benchmark algorithms in terms of classification accuracy (first rank) for seven (7) datasets (77.78%) and demonstrates the lowest number of selected features in six (6) datasets (66.67%). The proposed EGCACO algorithm can be utilised for FS in DNA microarray classification tasks that involve large dataset size in various application domains

Effective detection of security compromises in enterprises using feature engineering

We present a method to effectively detect malicious activity in the data of enterprise logs. Our method involves feature engineering, or generating new features by applying operators on the features of the raw data. We apply the Fourier expansion of Boolean functions to generate parity functions on feature subsets, or parity features. We also investigate a heuristic method of applying Boolean operators to raw data features, generating propositional features. We demonstrate with real data sets that the engineered features enhance the performance of classifiers and clustering algorithms. As compared to classification of raw data features, the engineered features achieve up to 50.6% improvement in malicious recall while sacrificing no more than 0.47% in accuracy. Clustering with respect to the engineered features finds up to 6 "pure" malicious clusters, as compared to 0 "pure" clusters with raw data features. In one case, exactly one (1) engineered feature could achieve higher performance than 91 raw data features. In general, a small number (<10) of engineered features achieve higher performance than raw data features