A graph-based feature selection method for learning to rank using spectral clustering for redundancy minimization and biased PageRank for relevance analysis

This paper addresses the feature selection problem in learning to rank (LTR). We propose a graph-based feature selection method, named FS-SCPR, which comprises four steps: (i) use ranking information to assess the similarity between features and construct an undirected feature similarity graph; (ii) apply spectral clustering to cluster features using eigenvectors of matrices extracted from the graph; (iii) utilize biased PageRank to assign a relevance score with respect to the ranking problem to each feature by incorporating each feature?s ranking performance as preference to bias the PageRank computation; and (iv) apply optimization to select the feature from each cluster with both the highest relevance score and most information of the features in the cluster. We also develop a new LTR for information retrieval (IR) approach that first exploits FS-SCPR as a preprocessor to determine discriminative and useful features and then employs Ranking SVM to derive a ranking model with the selected features. An evaluation, conducted using the LETOR benchmark datasets, demonstrated the competitive performance of our approach compared to representative feature selection methods and state-of-the-art LTR methods.

A hybrid deep learning model for predicting and targeting the less immunized area to improve childrens vaccination rate

There has been a major and rising interest in India for increasing vaccination rate among peoples to make the nation healthier and safer. In this paper, a new hybrid deep learning model is proposed to predict and target vaccination rates in the less immunized regions. The Rank-Based Multi-Layer Perceptron (R-MLP) hybrid deep learning framework uses the data collected from the recently updated District Level Household Survey-4 (DLHS). R-MLP model predicts and categorizes the percentage of partly immunized vaccination rates as extreme, low and medium ranges. This predicted findings are cross-verified by Deep Soft Cosine Semantic and Ranking SVM based model (DSS-RSM). DSS-RSM model uses the data obtained from the medical practitioners through a location-based social network. The proposed model predicts and extracts patterns with high similarity frequency for identifying vulnerable low immunization regions. It classifies the predicted patterns into two classes such as Class 1 is denoted as high ranked regions and Class 2 is denoted as low ranked regions based on the percentage of pattern matches. Finally, the results from R-MLP and DSS-RSM models are cross-linked together using ensemble model. This model finds the loss values to identify the target regions were health care program need to be conducted for increasing the level of immunization among children’s. The proposed hybrid deep learning models trains and validates using python-based Keras and TensorFlow deep learning libraries. The performance of the proposed hybrid deep learning model is compared with other variant machine learning techniques such as Decision Tree C5.0, Naive Bayes and Linear Regression. This comparative results are evaluated using evaluation measures such as Precision, Recall, Accuracy and F1-Measure. Our results show that the hybrid deep learning system is clearly superior to any other alternative approach.

Data Mining for Multicriteria Single Facility Location Problems

This chapter focuses on available data analysis and data mining techniques to find the optimal location of the Multicriteria Single Facility Location Problem (MSFLP) at diverse business settings. Solving for the optimal of an MSFLP, there exists numerous multicriteria decision analysis techniques. Mainstream models are mentioned in this chapter, while presenting a general classification of the MSFLP and its framework. Besides, topics from machine learning with respect to decision analysis are covered: Unsupervised Principal Components Analysis ranking (PCA-rank) and supervised Support Vector Machines ranking (SVM-rank). This chapter proposes a data mining perspective for the multicriteria single facility location problem and proposes a new approach to the facility location problem with the combination of the PCA-rank and ranking SVMs.

A High Performance Parallel Ranking SVM with OpenCL on Multi-core and Many-core Platforms

A ranking support vector machine (RSVM) is a typical pairwise method of learning to rank, which is effective in ranking problems. However, the training speed of RSVMs are not satisfactory, especially when solving large-scale data ranking problems. Recent years, many-core processing units (graphics processing unit (GPU), Many Integrated Core (MIC)) and multi-core processing units have exhibited huge superiority in the parallel computing domain. With the support of hardware, parallel programming develops rapidly. Open Computing Language (OpenCL) and Open Multi-Processing (OpenMP) are two of popular parallel programming interfaces. The authors present two high-performance parallel implementations of RSVM, an OpenCL version implemented on multi-core and many-core platforms, and an OpenMP version implemented on multi-core platform. The experimental results show that the OpenCL version parallel RSVM achieved considerable speedup on Intel MIC 7110P, NVIDIA Tesla K20M and Intel Xeon E5-2692v2, and it also shows good portability.

Unbiased Learning-to-Rank with Biased Feedback

Implicit feedback (e.g., clicks, dwell times, etc.) is an abundant source of data in human-interactive systems. While implicit feedback has many advantages (e.g., it is inexpensive to collect, user-centric, and timely), its inherent biases are a key obstacle to its effective use. For example, position bias in search rankings strongly influences how many clicks a result receives, so that directly using click data as a training signal in Learning-to-Rank (LTR) methods yields sub-optimal results. To overcome this bias problem, we present a counterfactual inference framework that provides the theoretical basis for unbiased LTR via Empirical Risk Minimization despite biased data. Using this framework, we derive a propensity-weighted ranking SVM for discriminative learning from implicit feedback, where click models take the role of the propensity estimator. Beyond the theoretical support, we show empirically that the proposed learning method is highly effective in dealing with biases, that it is robust to noise and propensity model mis-specification, and that it scales efficiently. We also demonstrate the real-world applicability of our approach on an operational search engine, where it substantially improves retrieval performance.