IJARAI Volume 5 Issue 9

Copyright Statement: This is an open access publication licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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Paper 1: A Rank Aggregation Algorithm for Ensemble of Multiple Feature Selection Techniques in Credit Risk Evaluation

Abstract: In credit risk evaluation the accuracy of a classifier is very significant for classifying the high-risk loan applicants correctly. Feature selection is one way of improving the accuracy of a classifier. It provides the classifier with important and relevant features for model development. This study uses the ensemble of multiple feature ranking techniques for feature selection of credit data. It uses five individual rank based feature selection methods. It proposes a novel rank aggregation algorithm for combining the ranks of the individual feature selection methods of the ensemble. This algorithm uses the rank order along with the rank score of the features in the ranked list of each feature selection method for rank aggregation. The ensemble of multiple feature selection techniques uses the novel rank aggregation algorithm and selects the relevant features using the 80%, 60%, 40% and 20% thresholds from the top of the aggregated ranked list for building the C4.5, MLP, C4.5 based Bagging and MLP based Bagging models. It was observed that the performance of models using the ensemble of multiple feature selection techniques is better than the performance of 5 individual rank based feature selection methods. The average performance of all the models was observed as best for the ensemble of feature selection techniques at 60% threshold. Also, the bagging based models outperformed the individual models most significantly for the 60% threshold. This increase in performance is more significant from the fact that the number of features were reduced by 40% for building the highest performing models. This reduces the data dimensions and hence the overall data size phenomenally for model building. The use of the ensemble of feature selection techniques using the novel aggregation algorithm provided more accurate models which are simpler, faster and easy to interpret.

Author 1: Shashi Dahiya
Author 2: S.S Handa
Author 3: N.P Singh

Keywords: Classification; Credit Risk; Feature Selection; Ensemble; Rank Aggregation; Bagging

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Paper 2: Pursuit Reinforcement Competitive Learning: PRCL based Online Clustering with Tracking Algorithm and its Application to Image Retrieval

Abstract: Pursuit Reinforcement guided Competitive Learning: PRCL based on relatively fast online clustering that allows grouping the data in concern into several clusters when the number of data and distribution of data are varied of reinforcement guided competitive learning is proposed. One of applications of the proposed method is image portion retrievals from the relatively large scale of the images such as Earth observation satellite images. It is found that the proposed method shows relatively fast on the retrievals in comparison to the other existing conventional online clustering such as Vector Quatization: VQ. Moreover, the proposed method shows much faster than the others for the multi-stage retrievals of image portion as well as scale estimation.

Author 1: Kohei Arai

Keywords: Pursuit Reinforcement Guided Competitive Learning; Reinforcement Guided Competitive Learning; Sustained Reinforcement Guided Competitive Learning Vector Quantization; Learning Automata

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Paper 3: Direction for Artificial Intelligence to Achieve Sapiency Inspired by Homo Sapiens

Abstract: Artificial intelligence technology has developed significantly in the past decades. Although many computational programs are able to approximate many cognitive abilities of Homo sapiens, the intelligence and sapience level of these programs are not even close to Homo sapiens. Rather than developing a computational system with the intelligent or sapient attribute, I propose to develop a system capable of performing functions that could deem as intelligent or sapient by Homo sapiens or others. I advocate converting current computational systems to educable systems that have built-in capabilities to learn and be taught with a universal programming language. The idea is that this attempt would help to attain computational actions in artificial means, which could be viewed as similar to human intelligent and sapient acts. Although this paper is seemingly speculative, some feasible elements are proposed to advance the field of Artificial Intelligence.

Author 1: Mahmud Arif Pavel

Keywords: artificial sapience; sapient agent; artificial intelligence; bio-inspired AI

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Paper 4: Prediction of Employee Turnover in Organizations using Machine Learning Algorithms

Abstract: Employee turnover has been identified as a key issue for organizations because of its adverse impact on work place productivity and long term growth strategies. To solve this problem, organizations use machine learning techniques to predict employee turnover. Accurate predictions enable organizations to take action for retention or succession planning of employees. However, the data for this modeling problem comes from HR Information Systems (HRIS); these are typically under-funded compared to the Information Systems of other domains in the organization which are directly related to its priorities. This leads to the prevalence of noise in the data that renders predictive models prone to over-fitting and hence inaccurate. This is the key challenge that is the focus of this paper, and one that has not been addressed historically. The novel contribution of this paper is to explore the application of Extreme Gradient Boosting (XGBoost) technique which is more robust because of its regularization formulation. Data from the HRIS of a global retailer is used to compare XGBoost against six historically used supervised classifiers and demonstrate its significantly higher accuracy for predicting employee turnover.

Author 1: Rohit Punnoose
Author 2: Pankaj Ajit

Keywords: turnover prediction; machine learning; extreme gradient boosting; supervised classification; regularization

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Paper 5: WSDF: Weighting of Signed Distance Function for Camera Motion Estimation in RGB-D Data

Abstract: With the recent advent of the cost-effective Kinect, which can capture real-time high-resolution RGB and visual depth information, has opened an opportunity to significantly increase the capabilities of many automated vision based recognition including object/action classification, 3D reconstruction, etc… In this work, we address the camera motion estimation which is an important phase in 3D object reconstruction system based on RGB-D data. We segment objects by thresholding algorithm based on depth data and propose the weighting function for SDF that is called WSDF. The problem of minimizing of this function is solved by Gauss-Newton methods. We systematically evaluate our method on TUM dataset. The experimental results are measured by ATE and RPE that evaluate both global and local consistency of camera motion estimation algorithm. We demonstrate large improvements over the state-of-the-art methods on both plant and teddy3 objects and achieve the best ATE as 0.00564 and 0.0182 and the best RPE as 0.00719 and 0.00104, respectively. These experiments show that the proposed method significantly outperforms state-of-the-art techniques.

Author 1: Pham Minh Hoang
Author 2: Vo Hoai Viet
Author 3: Ly Quoc Ngoc

Keywords: RGB-D data; 3D Reconstruction; SDF; Camera Motion Estimation

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