classifier combination

Unemployment insurance is one of the most popular insurance types in the modern world. The Social Security Organization is responsible for checking the unemployment benefits of individuals supported by unemployment insurance. Hand-crafted evaluation of unemployment claims requires a big deal of time and money. Data mining and machine learning as two efficient tools for data analysis can assist Social Security Organization in automating this process. In this research work, a hybrid supervised learning method is proposed to verify the eligibility of applicants for unemployment. The proposed method takes as input the information of insured individuals, and assigns a numeric score to each applicant through analyzing the input data. Then, claimants are classified into two groups according to those scores: "Qualified” and "Unqualified". The proposed method includes two hybrid strategies: BSA-SVM and combination of confidence values. In BSA-SVM method, backtracking search algorithm (BSA) is used to estimate the prameters of support vector machines (SVM) and improves the classification performance. In the second approach, confidence values extracted from individual classofiers are combined to better classify the input data. Empirical evaluation shows an accuracy of 87% for BSA-SVM and 86% for the second approach.

Content based image retrieval (CBIR) applies machine vision techniques to extract similar images for a given query image. The main challenge of CBIR is the semantic gap between low level pixel and segment based features and high-level concepts in the image. An approach towards reducing this gaps is to use high level region and object based features. However, the low-level features describe image details and enforce between image discriminations. Accordingly, it is expected that the use of both feature types will lead to better results. This paper tries to reduce the mentioned gap by combining decision results at four granularities, namely pixel, region, object, and concept levels. Pixel level retrieval adopts SIFT features and local binary patterns. Region level subsystem partitions the image into a set of segments and extracts their color and texture features using hue descriptor and Gabor filters for subsequent processing. AlexNet convolutional neural network is employed for object based retrieval. Word2vec embedding is used for concept level retrieval that exploits conceptual relations between objects to enhance the retrieval results. Experiments over Wang and GHIM datasets confirm the feasibility of the proposed combination and conclude that it improves overall performance of the retrieval system.