Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

An apposite and eminently readable reference for all behavioral science research and development

Encyclopedia of Statistics in Behavioral Science

A Comprehensive Review on Multi-objective Optimization Techniques: Past, Present and Future

To deal with semi-supervised feature selection tasks, this paper presents a recursive feature retention (RFR) method based on a neighborhood discriminant index (NDI) method (a supervised feature selection method) and a forward iterative Laplacian score (FILS) method (an unsupervised method), where FILS is designed specially for RFR. The goal of RFR is to determine an optimal feature subset that has not only a high discriminant ability but also a strong ability to maintain the local structure of data. The discriminant ability of a feature is measured by NDI, and the ability of a feature to maintain the local structure of data is described by FILS. RFR compromises these two scores to give a balanced score for a feature. RFR iteratively selects a feature with the smallest balanced score and moves it into the current optimal feature subset. This paper also shows theoretical analysis to speed up iterations. Extensive experiments are conducted on toy and real-world data sets. Experimental results confirm that RFR can achieve a better performance compared with the state-of-the-art semi-supervised methods.

A recursive feature retention method for semi-supervised feature selection

In multi-label learning, exploiting label correlation, alleviating class imbalance and learning label-specific features have been hot topics to increase classification performance. In the paper, we propose a method to address the three issues simultaneously. The method, named LPLC-LA, builds a Bayesian model by exploiting the local positive and negative label correlations with label awareness. LPLC-LA consists of extracting label-specific features to obtain the local positive and negative correlation, defining two label aware weights for label imbalance and label separability, and then improving the estimation of label conditional probability through the two weights. The experimental results over eight benchmark datasets show that LPLC-LA can achieve better performance compared with other state-of-the-art approaches.

Local positive and negative label correlation analysis with label awareness for multi-label classification

There are few studies in the literature to address the multi-objective multi-label feature selection for the classification of video data using evolutionary algorithms Selecting the most appropriate subset of features is a significant problem while maintaining/improving the accuracy of the prediction results This study proposes a framework of parallel multi-objective Non-dominated Sorting Genetic Algorithms (NSGA-II) for exploring a Pareto set of non-dominated solutions The subsets of non-dominated features are extracted and validated by multi-label classification techniques, Binary Relevance (BR), Classifier Chains (CC), Pruned Sets (PS), and Random k-Labelset (RAkEL) Base classifiers such as Support Vector Machines (SVM), J48-Decision Tree (J48), and Logistic Regression (LR) are performed in the classification phase of the algorithms Comprehensive experiments are carried out with local feature descriptors extracted from two multi-label data sets, the well-known MIR-Flickr dataset and a Wireless Multimedia Sensor (WMS) dataset that we have generated from our video recordings The prediction accuracy levels are improved by 636% and 257% for the MIR-Flickr and WMS datasets respectively while the number of features is significantly reduced The results verify that the algorithms presented in this new framework outperform the state-of-the-art algorithms

A new framework of multi-objective evolutionary algorithms for feature selection and multi-label classification of video data

It is of great importance to extract and validate an optimal subset of non-dominated features for effective multi-label classification. However, deciding on the best subset of features is an NP-Hard problem and plays a key role in improving the prediction accuracy and the processing time of video datasets. In this study, we propose autoencoders for dimensionality reduction of video data sets and ensemble the features extracted by the multi-objective evolutionary Non-dominated Sorting Genetic Algorithm and the autoencoder. We explore the performance of well-known multi-label classification algorithms for video datasets in terms of prediction accuracy and the number of features used. More specifically, we evaluate Non-dominated Sorting Genetic Algorithm-II, autoencoders, ensemble learning algorithms, Principal Component Analysis, Information Gain, and Correlation Based Feature Selection. Some of these algorithms use feature selection techniques to improve the accuracy of the classification. Experiments are carried out with local feature descriptors extracted from two multi-label datasets, the MIR-Flickr dataset which consists of images and the Wireless Multimedia Sensor dataset that we have generated from our video recordings. Significant improvements in the accuracy performance of the algorithms are observed while the number of features is being reduced.

/pdf/analysis-of-multiobjective-algorithms-for-the-classification-25dpx6rmq8.pdf

Gizem Nur Karagoz

Papers

A new framework of multi-objective evolutionary algorithms for feature selection and multi-label classification of video data

Analysis of Multiobjective Algorithms for the Classification of Multi-Label Video Datasets