Analisis dan Implementasi Generalized Fisher Score

ABSTRAKSI: Data merupakan bahan utama dalam mendapatkan Informasi. Pengolahan data yang tepat dapat berimbas pada informasi bahkan pengetahuan yang berguna. Salah satu cara untuk mendapatkan pengetahuan dari suatu data adalah dengan proses klasifikasi. Proses klasifikasi sangat dipengaruhi oleh kondisi data. Kondisi data yang bersih bebas noise akan menyebabkan data dapat terklasifikasi dengan baik. Data dengan dimensi tinggi dapat dipastikan akan memiliki waktu proses yang sangat besar. Salah satu noise yang sering dijumpai adalah adanya irrelevant dan redundant feature.Salah satu cara yang biasa digunakan untuk menyelesaikan permasalahan adanya irrelevant dan redundant feature adalah dengan menggunakan feature selection. salah satu metode feature selection yang cukup terkenal adalah Fisher score, tetapi fisher score sendiri memiliki beberapa kelemahan diantaranya adalah tidak dapat menangani redundant feature dan tidak dapat mengani kemungkinan terdapatnya subset feature pada suatu data.Pada Tugas Akhir ini, akan dibahas sebuah metode yang menangani kelemahan dari metode Fisher score, yaitu Generalized Fisher Score dan dilakukan pula analisis terhadap peforma metode Generalized Fisher Score sebagai feature selection. Selain itu pada tugas akhir ini juga mencoba memperbaiki kelemahan yang ada pada Generalized Fisher Score dengan memberikan modifikasi pada Generalized Fisher Score.Kata Kunci : Data Mining, Klasifikasi, feature selection, Generalized Fisher Score.ABSTRACT: The data is a key ingredient in getting information. Proper data processing can affect even the knowledge of useful information. One way to gain knowledge from the data is the process of classification. Classification process is strongly influenced by the condition of the data. Conditions clean noise-free data will cause data to be classified properly. With high-dimensional data certainly will have a very big process.One of the common ways to solve the problem are irrelevant and redundant features using feature selection. Feature selection is a process of selecting relevant features for a classification process. one of the methods well-known feature selection is Fisher score, but the fisher score itself has some drawbacks such as it can’t handle redundant features and can’t hold a possibility of the presence of a subset of features in the data.In this final project, we discuss a method that handles the weakness of the method of Fisher score, namely the Generalized Fisher Score and also conducted an analysis of the Performance of Generalized Fisher score methods as a feature selection. In addition to the final project is also trying to fix weaknesses in Generalized Fisher scores by providing modifications to the Generalized Fisher Score.Keyword: Data Mining, classification, feature selection, Generalized Fisher Score

Emotion Recognition in Low-Resource Settings:An Evaluation of Automatic Feature Selection Methods

Research in automatic affect recognition has seldom addressed the issue of computational resource utilization. With the advent of ambient intelligence technology which employs a variety of low-power, resource-constrained devices, this issue is increasingly gaining interest. This is especially the case in the context of health and elderly care technologies, where interventions may rely on monitoring of emotional status to provide support or alert carers as appropriate. This paper focuses on emotion recognition from speech data, in settings where it is desirable to minimize memory and computational requirements. Reducing the number of features for inductive inference is a route towards this goal. In this study, we evaluate three different state-of-the-art feature selection methods: Infinite Latent Feature Selection (ILFS), ReliefF and Fisher (generalized Fisher score), and compare them to our recently proposed feature selection method named `Active Feature Selection' (AFS). The evaluation is performed on three emotion recognition data sets (EmoDB, SAVEE and EMOVO) using two standard acoustic paralinguistic feature sets (i.e. eGeMAPs and emobase). The results show that similar or better accuracy can be achieved using subsets of features substantially smaller than the entire feature set. A machine learning model trained on a smaller feature set will reduce the memory and computational resources of an emotion recognition system which can result in lowering the barriers for use of health monitoring technology

An Effective Feature Selection Method Based on Pair-Wise Feature Proximity for High Dimensional Low Sample Size Data

Feature selection has been studied widely in the literature. However, the efficacy of the selection criteria for low sample size applications is neglected in most cases. Most of the existing feature selection criteria are based on the sample similarity. However, the distance measures become insignificant for high dimensional low sample size (HDLSS) data. Moreover, the variance of a feature with a few samples is pointless unless it represents the data distribution efficiently. Instead of looking at the samples in groups, we evaluate their efficiency based on pairwise fashion. In our investigation, we noticed that considering a pair of samples at a time and selecting the features that bring them closer or put them far away is a better choice for feature selection. Experimental results on benchmark data sets demonstrate the effectiveness of the proposed method with low sample size, which outperforms many other state-of-the-art feature selection methods.Comment: European Signal Processing Conference 201

Selecting a Small Set of Optimal Gestures from an Extensive Lexicon

Finding the best set of gestures to use for a given computer recognition problem is an essential part of optimizing the recognition performance while being mindful to those who may articulate the gestures. An objective function, called the ellipsoidal distance ratio metric (EDRM), for determining the best gestures from a larger lexicon library is presented, along with a numerical method for incorporating subjective preferences. In particular, we demonstrate an efficient algorithm that chooses the best $n$ gestures from a lexicon of $m$ gestures where typically $n \ll m$ using a weighting of both subjective and objective measures.Comment: 27 pages, 7 figure

Effective Discriminative Feature Selection with Non-trivial Solutions

Feature selection and feature transformation, the two main ways to reduce dimensionality, are often presented separately. In this paper, a feature selection method is proposed by combining the popular transformation based dimensionality reduction method Linear Discriminant Analysis (LDA) and sparsity regularization. We impose row sparsity on the transformation matrix of LDA through ${\ell}_{2,1}$-norm regularization to achieve feature selection, and the resultant formulation optimizes for selecting the most discriminative features and removing the redundant ones simultaneously. The formulation is extended to the ${\ell}_{2,p}$-norm regularized case: which is more likely to offer better sparsity when $0<p<1$. Thus the formulation is a better approximation to the feature selection problem. An efficient algorithm is developed to solve the ${\ell}_{2,p}$-norm based optimization problem and it is proved that the algorithm converges when $0<p\le 2$. Systematical experiments are conducted to understand the work of the proposed method. Promising experimental results on various types of real-world data sets demonstrate the effectiveness of our algorithm