• Title/Summary/Keyword: Feature selection algorithm

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Development of Interactive Feature Selection Algorithm(IFS) for Emotion Recognition

  • Yang, Hyun-Chang;Kim, Ho-Duck;Park, Chang-Hyun;Sim, Kwee-Bo
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.6 no.4
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    • pp.282-287
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    • 2006
  • This paper presents an original feature selection method for Emotion Recognition which includes many original elements. Feature selection has some merits regarding pattern recognition performance. Thus, we developed a method called thee 'Interactive Feature Selection' and the results (selected features) of the IFS were applied to an emotion recognition system (ERS), which was also implemented in this research. The innovative feature selection method was based on a Reinforcement Learning Algorithm and since it required responses from human users, it was denoted an 'Interactive Feature Selection'. By performing an IFS, we were able to obtain three top features and apply them to the ERS. Comparing those results from a random selection and Sequential Forward Selection (SFS) and Genetic Algorithm Feature Selection (GAFS), we verified that the top three features were better than the randomly selected feature set.

Genetic Algorithm Based Feature Selection Method Development for Pattern Recognition (패턴 인식문제를 위한 유전자 알고리즘 기반 특징 선택 방법 개발)

  • Park Chang-Hyun;Kim Ho-Duck;Yang Hyun-Chang;Sim Kwee-Bo
    • Journal of the Korean Institute of Intelligent Systems
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    • v.16 no.4
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    • pp.466-471
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    • 2006
  • IAn important problem of pattern recognition is to extract or select feature set, which is included in the pre-processing stage. In order to extract feature set, Principal component analysis has been usually used and SFS(Sequential Forward Selection) and SBS(Sequential Backward Selection) have been used as a feature selection method. This paper applies genetic algorithm which is a popular method for nonlinear optimization problem to the feature selection problem. So, we call it Genetic Algorithm Feature Selection(GAFS) and this algorithm is compared to other methods in the performance aspect.

Optimal k-Nearest Neighborhood Classifier Using Genetic Algorithm (유전알고리즘을 이용한 최적 k-최근접이웃 분류기)

  • Park, Chong-Sun;Huh, Kyun
    • Communications for Statistical Applications and Methods
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    • v.17 no.1
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    • pp.17-27
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    • 2010
  • Feature selection and feature weighting are useful techniques for improving the classification accuracy of k-Nearest Neighbor (k-NN) classifier. The main propose of feature selection and feature weighting is to reduce the number of features, by eliminating irrelevant and redundant features, while simultaneously maintaining or enhancing classification accuracy. In this paper, a novel hybrid approach is proposed for simultaneous feature selection, feature weighting and choice of k in k-NN classifier based on Genetic Algorithm. The results have indicated that the proposed algorithm is quite comparable with and superior to existing classifiers with or without feature selection and feature weighting capability.

ModifiedFAST: A New Optimal Feature Subset Selection Algorithm

  • Nagpal, Arpita;Gaur, Deepti
    • Journal of information and communication convergence engineering
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    • v.13 no.2
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    • pp.113-122
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    • 2015
  • Feature subset selection is as a pre-processing step in learning algorithms. In this paper, we propose an efficient algorithm, ModifiedFAST, for feature subset selection. This algorithm is suitable for text datasets, and uses the concept of information gain to remove irrelevant and redundant features. A new optimal value of the threshold for symmetric uncertainty, used to identify relevant features, is found. The thresholds used by previous feature selection algorithms such as FAST, Relief, and CFS were not optimal. It has been proven that the threshold value greatly affects the percentage of selected features and the classification accuracy. A new performance unified metric that combines accuracy and the number of features selected has been proposed and applied in the proposed algorithm. It was experimentally shown that the percentage of selected features obtained by the proposed algorithm was lower than that obtained using existing algorithms in most of the datasets. The effectiveness of our algorithm on the optimal threshold was statistically validated with other algorithms.

Feature Combination and Selection Using Genetic Algorithm for Character Recognition (유전 알고리즘을 이용한 특징 결합과 선택)

  • Lee Jin-Seon
    • The Journal of the Korea Contents Association
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    • v.5 no.5
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    • pp.152-158
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    • 2005
  • By using a combination of different feature sets extracted from input character patterns, we can improve the character recognition system performance. To reduce the dimensionality of the combined feature vector, we conduct the feature selection. This paper proposes a general framework for the feature combination and selection for character recognition problems. It also presents a specific design for the handwritten numeral recognition. Tn the design, DDD and AGD feature sets are extracted from handwritten numeral patterns, and a genetic algorithm is used for the feature selection. Experimental result showed a significant accuracy improvement by about 0.7% for the CENPARMI handwrittennumeral database.

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Feature Subset Selection Algorithm based on Entropy (엔트로피를 기반으로 한 특징 집합 선택 알고리즘)

  • 홍석미;안종일;정태충
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.41 no.2
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    • pp.87-94
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    • 2004
  • The feature subset selection is used as a preprocessing step of a teaming algorithm. If collected data are irrelevant or redundant information, we can improve the performance of learning by removing these data before creating of the learning model. The feature subset selection can also reduce the search space and the storage requirement. This paper proposed a new feature subset selection algorithm that is using the heuristic function based on entropy to evaluate the performance of the abstracted feature subset and feature selection. The ACS algorithm was used as a search method. We could decrease a size of learning model and unnecessary calculating time by reducing the dimension of the feature that was used for learning.

Feature Selection and Performance Analysis using Quantum-inspired Genetic Algorithm (양자 유전알고리즘을 이용한 특징 선택 및 성능 분석)

  • Heo, G.S.;Jeong, H.T.;Park, A.;Baek, S.J.
    • Smart Media Journal
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    • v.1 no.1
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    • pp.36-41
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    • 2012
  • Feature selection is the important technique of selecting a subset of relevant features for building robust pattern recognition systems. Various methods have been studied for feature selection from sequential search algorithms to stochastic algorithms. In this work, we adopted a Quantum-inspired Genetic Algorithm (QGA) which is based on the concept and principles of quantum computing such as Q-bits and superposition of state for feature selection. The performance of QGA is compared to that of the Conventional Genetic Algorithm (CGA) with respect to the classification rates and the number of selected features. The experimental result using UCI data sets shows that QGA is superior to CGA.

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A Fast and Efficient Haar-Like Feature Selection Algorithm for Object Detection (객체검출을 위한 빠르고 효율적인 Haar-Like 피쳐 선택 알고리즘)

  • Chung, Byung Woo;Park, Ki-Yeong;Hwang, Sun-Young
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.38A no.6
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    • pp.486-491
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    • 2013
  • This paper proposes a fast and efficient Haar-like feature selection algorithm for training classifier used in object detection. Many features selected by Haar-like feature selection algorithm and existing AdaBoost algorithm are either similar in shape or overlapping due to considering only feature's error rate. The proposed algorithm calculates similarity of features by their shape and distance between features. Fast and efficient feature selection is made possible by removing selected features and features with high similarity from feature set. FERET face database is used to compare performance of classifiers trained by previous algorithm and proposed algorithm. Experimental results show improved performance comparing classifier trained by proposed method to classifier trained by previous method. When classifier is trained to show same performance, proposed method shows 20% reduction of features used in classification.

A study of creative human judgment through the application of machine learning algorithms and feature selection algorithms

  • Kim, Yong Jun;Park, Jung Min
    • International journal of advanced smart convergence
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    • v.11 no.2
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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.

Feature Selection for Multi-Class Genre Classification using Gaussian Mixture Model (Gaussian Mixture Model을 이용한 다중 범주 분류를 위한 특징벡터 선택 알고리즘)

  • Moon, Sun-Kuk;Choi, Tack-Sung;Park, Young-Cheol;Youn, Dae-Hee
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.32 no.10C
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    • pp.965-974
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    • 2007
  • In this paper, we proposed the feature selection algorithm for multi-class genre classification. In our proposed algorithm, we developed GMM separation score based on Gaussian mixture model for measuring separability between two genres. Additionally, we improved feature subset selection algorithm based on sequential forward selection for multi-class genre classification. Instead of setting criterion as entire genre separability measures, we set criterion as worst genre separability measure for each sequential selection step. In order to assess the performance proposed algorithm, we extracted various features which represent characteristics such as timbre, rhythm, pitch and so on. Then, we investigate classification performance by GMM classifier and k-NN classifier for selected features using conventional algorithm and proposed algorithm. Proposed algorithm showed improved performance in classification accuracy up to 10 percent for classification experiments of low dimension feature vector especially.