• International Journal of Fuzzy Logic and Intelligent Systems
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• 제13권1호
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• pp.12-18
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• 2013

Motor imagery classification in electroencephalography (EEG)-based brain-computer interface (BCI) systems is an important research area. To simplify the complexity of the classification, selected power bands and electrode channels have been widely used to extract and select features from raw EEG signals, but there is still a loss in classification accuracy in the state-of- the-art approaches. To solve this problem, we propose a discriminative feature extraction algorithm based on power bands with principle component analysis (PCA). First, the raw EEG signals from the motor cortex area were filtered using a bandpass filter with ${\mu}$ and ${\beta}$ bands. This research considered the power bands within a 0.4 second epoch to select the optimal feature space region. Next, the total feature dimensions were reduced by PCA and transformed into a final feature vector set. The selected features were classified by applying a support vector machine (SVM). The proposed method was compared with a state-of-art power band feature and shown to improve classification accuracy.

• 복식
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• 제50권5호
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• pp.77-90
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• 2000

The purpose of this study is to disclose the dimension of job characteristic, working place situation. and clothes selection, which are variable on clothes selection, to research the relation of job characteristic, working place situation, and clothes selection and to disclose the feature of consumers by each group in accordance with job characteristic. The follows are summary of this study result : The concept structure of job characteristic is composed by four types. Working place situation related with clothes selection are consisted of five types. Professional women selected clothes in accordance with nine feature. Job characteristic is related with working palace situation and influences on clothes selection. The persons influenced by activity state regarded personality as important when select clothes. The persons influenced by duty state regarded harmony and control as important, instead of, they ignored conformity. People select occupation according to individual feature, so the feature of consumers, working place environment, working place situation are different in accordance with job characteristic, as a result we can find differences in clothes selection suitable for working palace and purchasing.

• Nuclear Engineering and Technology
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• 제33권5호
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• pp.457-467
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• 2001

It is well known that the performance of a fuzzy neural network strongly depends on the input features selected for its training. In its applications to sensor signal estimation, there are a large number of input variables related with an output As the number of input variables increases, the training time of fuzzy neural networks required increases exponentially. Thus, it is essential to reduce the number of inputs to a fuzzy neural network and to select the optimum number of mutually independent inputs that are able to clearly define the input-output mapping. In this work, principal component analysis (PCA), genetic algorithms (CA) and probability theory are combined to select new important input features. A proposed feature selection method is applied to the signal estimation of the steam generator water level, the hot-leg flowrate, the pressurizer water level and the pressurizer pressure sensors in pressurized water reactors and compared with other input feature selection methods.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제13권4호
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• pp.284-290
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• 2013

Feature detection is very important to image processing area. In this paper we compare and analyze some characteristics of image processing algorithms for corner and blob feature detection. We also analyze the simulation results through image matching process. We show that how these algorithms work and how fast they execute. The simulation results are shown for helping us to select an algorithm or several algorithms extracting corner and blob feature.

• 정보처리학회논문지B
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• 제14B권2호
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• pp.135-140
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• 2007

특징선택은 문제 영역에서 관찰된 다차원데이터로부터 데이터가 묘사하는 구조를 잘 반영하는 속성을 선택하여 효과적인 실험 데이터를 구성하는 데이터 준비과정이다. 이 과정은 문서분류, 영상인식, 유전자 선택 분야에서의 같은 분류시스템의 성능향상에 중요한 구성요소로서 상관관계 기법, 차원축소 및 상호 정보 처리 등의 통계학이나 정보이론의 접근방법을 중심으로 연구되어왔다. 이와 같은 선택 분야의 연구는 다루는 데이터의 양이 방대해지고 복잡해지면서 더욱 중요시 되고 있다. 본 논문에서는 데이터가 가지는 특성을 반영하면서 새로운 데이터에 대하여 일반화 할 수 있는 특징선택 방법을 제안하고자 한다. 준비된 데이터의 각 속성 데이터에 대하여 퍼지 클러스터 분석에 의하여 최적의 클러스터 정보를 얻고 이를 바탕으로 근접성과 분리성의 경로를 측정하여 그 값에 따라 특징을 선택하는 매카니즘을 제공한다. 제안된 방법을 실세계의 컴퓨터 바이러스 분류에 적용하여 기존의 대비에 의한 휴리스틱 방법에 의해 선택된 데이터를 가지고 분류한 것과 비교하고자 한다. 이를 통하여 주어진 특징에 시연을 부여할 수 있고 효과적으로 특징을 선택하여 시스템의 성능을 향상 시킬 수 있음을 확인한다.

• Journal of Advanced Marine Engineering and Technology
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• 제40권5호
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• pp.437-446
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• 2016

Feature selection has become an essential technique to reduce the dimensionality of data sets. Many features are frequently irrelevant or redundant for the classification tasks. The purpose of feature selection is to select relevant features and remove irrelevant and redundant features. Applications of the feature selection range from text processing, face recognition, bioinformatics, speaker verification, and medical diagnosis to financial domains. In this study, we focus on filter methods based on information entropy : IG (Information Gain), FCBF (Fast Correlation Based Filter), and mRMR (minimum Redundancy Maximum Relevance). FCBF has the advantage of reducing computational burden by eliminating the redundant features that satisfy the condition of approximate Markov blanket. However, FCBF considers only the relevance between the feature and the class in order to select the best features, thus failing to take into consideration the interaction between features. In this paper, we propose an improved FCBF to overcome this shortcoming. We also perform a comparative study to evaluate the performance of the proposed method.

• Journal of Computing Science and Engineering
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• 제11권4호
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• pp.130-141
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• 2017

Feature selection is one of the most challenging problems of pattern recognition and data mining. In this paper, a feature selection algorithm based on an improved version of binary differential evolution is proposed. The method simultaneously optimizes two feature selection criteria, namely, set approximation accuracy of rough set theory and relational algebra based derived score, in order to select the most relevant feature subset from an entire feature set. Superiority of the proposed method over other state-of-the-art methods is confirmed by experimental results, which is conducted over seven publicly available benchmark datasets of different characteristics such as a low number of objects with a high number of features, and a high number of objects with a low number of features.

• 말소리와 음성과학
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• 제2권1호
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• pp.77-85
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• 2010

This paper proposes an efficient feature vector processing technique to guard the Speech Emotion Recognition (SER) system against a variety of noises. In the proposed approach, emotional feature vectors are extracted from speech processed by comb filtering. Then, these extracts are used in a robust model construction based on feature vector classification. We modify conventional comb filtering by using speech presence probability to minimize drawbacks due to incorrect pitch estimation under background noise conditions. The modified comb filtering can correctly enhance the harmonics, which is an important factor used in SER. Feature vector classification technique categorizes feature vectors into either discriminative vectors or non-discriminative vectors based on a log-likelihood criterion. This method can successfully select the discriminative vectors while preserving correct emotional characteristics. Thus, robust emotion models can be constructed by only using such discriminative vectors. On SER experiment using an emotional speech corpus contaminated by various noises, our approach exhibited superior performance to the baseline system.

• International journal of advanced smart convergence
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• 제11권2호
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• pp.38-43
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• 2022

In this study, there are many difficulties in defining and judging creative people because there is no systematic analysis method using accurate standards or numerical values. Analyze and judge whether In the previous study, A study on the application of rule success cases through machine learning algorithm extraction, a case study was conducted to help verify or confirm the psychological personality test and aptitude test. We proposed a solution to a research problem in psychology using machine learning algorithms, Data Mining's Cross Industry Standard Process for Data Mining, and CRISP-DM, which were used in previous studies. After that, this study proposes a solution that helps to judge creative people by applying the feature selection algorithm. In this study, the accuracy was found by using seven feature selection algorithms, and by selecting the feature group classified by the feature selection algorithms, and the result of deriving the classification result with the highest feature obtained through the support vector machine algorithm was obtained.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.314-319
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• 1998

In this paper, for efficient data classification in multispectral bands environment, a band feature extraction method using the Rough sets theroy is proposed. First, we make a look up table from training data, and analyze the properties of experimental multispectral image data, then select the efficient band usin indiscernibility relation of Rough sets theory from analysis results. Proposed method is applied to LAMDSAT TM data on 2, June, 1992. Among them, normal distributive data were experimented, mainly. From this, we show clustering trends that similar to traditional band selection results by wavelength properties, from this, we verify that can use the proposed method that centered on data properties to select the efficient bands, though data sensing environment change to hyperspectral band environments.