Feature selection using Principal Component Analysis for massive retweet detection

Morchid, Mohamed; Dufour, Richard; Bousquet, Pierre-Michel; Linarès, Georges; Torres‐Moreno, Juan‐Manuel

doi:10.1016/j.patrec.2014.05.020

Cited by 51 publications

(27 citation statements)

References 18 publications

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“…Our method consists of two steps, which are depicted in In the first step, we perform a principal component analysis (PCA) and subsequently an orthogonal rotation on the complete set of meta-features that may be available. Even though this is a standard method in exploratory factor analysis, and has been used on different occasions (Morchid et al, 2014), this is the first time that it is applied at the meta-feature level. Hence, the input required by this step is a meta-dataset consisting of dataset characteristics (meta-features).…”

Section: Predictive Power Of Meta-featuresmentioning

confidence: 99%

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On the predictive power of meta-features in OpenML

Bilalli

Abelló

Aluja-Banet

2017

International Journal of Applied Mathematics and Computer Science

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The demand for performing data analysis is steadily rising. As a consequence, people of different profiles (i.e., nonexperienced users) have started to analyze their data. However, this is challenging for them. A key step that poses difficulties and determines the success of the analysis is data mining (model/algorithm selection problem). Meta-learning is a technique used for assisting non-expert users in this step. The effectiveness of meta-learning is, however, largely dependent on the description/characterization of datasets (i.e., meta-features used for meta-learning). There is a need for improving the effectiveness of meta-learning by identifying and designing more predictive meta-features. In this work, we use a method from exploratory factor analysis to study the predictive power of different meta-features collected in OpenML, which is a collaborative machine learning platform that is designed to store and organize meta-data about datasets, data mining algorithms, models and their evaluations. We first use the method to extract latent features, which are abstract concepts that group together meta-features with common characteristics. Then, we study and visualize the relationship of the latent features with three different performance measures of four classification algorithms on hundreds of datasets available in OpenML, and we select the latent features with the highest predictive power. Finally, we use the selected latent features to perform meta-learning and we show that our method improves the meta-learning process. Furthermore, we design an easy to use application for retrieving different meta-data from OpenML as the biggest source of data in this domain.

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Section: Predictive Power Of Meta-featuresmentioning

confidence: 99%

“…In words, PCA seeks to reduce the dimension of a large number of directly observable features into a smaller set of indirectly observable ones-latent features. More precisely, the goals (Morchid et al, 2014) of PCA are the following:…”

Section: Predictive Power Of Meta-featuresmentioning

confidence: 99%

On the predictive power of meta-features in OpenML

Bilalli

Abelló

Aluja-Banet

2017

International Journal of Applied Mathematics and Computer Science

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“…19,21,25,44 PCA can be used to reduce the dimensions of a multi-dimensional data set down into its basic components excluding any unnecessary information. It transforms uncorrelated component from the covariance matrix of the original data into a projection vector by maintaining as many variances as possible.…”

Section: Principal Component Analysismentioning

confidence: 99%

A Hybrid Approach for Person Identification Using Palmprint and Face Biometrics

Farmanbar

Toygar

2015

Int. J. Patt. Recogn. Artif. Intell.

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This paper proposes hybrid approaches based on both feature level and score level fusion strategies to provide a robust recognition system against the distortions of individual modalities. In order to compare the proposed schemes, a virtual multimodal database is formed from FERET face and PolyU palmprint databases. The proposed hybrid systems concatenate features extracted by local and global feature extraction methods such as Local Binary Patterns, Log Gabor, Principal Component Analysis and Linear Discriminant Analysis. Match score level fusion is performed in order to show the e®ectiveness and accuracy of the proposed schemes. The experimental results based on these databases reported a signi¯cant improvement of the proposed schemes compared with unimodal systems and other multimodal face-palmprint fusion methods.

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“…Among the most popular methods are principal component analysis (PCA) for dimensionality reduction [20] or association rules mining [1]. Other approaches to social media big data are sentiment analysis, keywords search, trends analysis [6,12,[21][22][23][24][25], and various social graph analysis metrics (using shares, likes, follows, etc.)…”

Section: Introductionmentioning

confidence: 99%