The complex systems with edge computing require a huge amount of multi-feature data to extract appropriate insights for their decision making, so it is important to find a feasible feature selection method to improve the computational efficiency and save the resource consumption. In this paper, a quantum-based feature selection algorithm for the multi-classification problem, namely, QReliefF, is proposed, which can effectively reduce the complexity of algorithm and improve its computational efficiency. First, all features of each sample are encoded into a quantum state by performing operations CMP and R_y, and then the amplitude estimation is applied to calculate the similarity between any two quantum states (i.e., two samples). According to the similarities, the Grover-Long method is utilized to find the nearest k neighbor samples, and then the weight vector is updated. After a certain number of iterations through the above process, the desired features can be selected with regards to the final weight vector and the threshold {\tau}. Compared with the classical ReliefF algorithm, our algorithm reduces the complexity of similarity calculation from O(MN) to O(M), the complexity of finding the nearest neighbor from O(M) to O(sqrt(M)), and resource consumption from O(MN) to O(MlogN). Meanwhile, compared with the quantum Relief algorithm, our algorithm is superior in finding the nearest neighbor, reducing the complexity from O(M) to O(sqrt(M)). Finally, in order to verify the feasibility of our algorithm, a simulation experiment based on Rigetti with a simple example is performed.

The recent advancements in machine learning have motivated researchers to generate classification models dealing with hundreds of classes such as in the case of image datasets. However, visualization of classification models with high number of classes and inter-model comparison in such classification problems are two areas that have not received much attention in the literature, despite the ever-increasing use of classification models to address problems with very large class categories. In this paper, we present our interactive visual analytics tool, called Circles, that allows a visual inter-model comparison of numerous classification models with 1K classes in one view. To mitigate the tricky issue of visual clutter, we chose concentric a radial line layout for our inter-model comparison task. Our prototype shows the results of 9 models with 1K classes

This will sound familiar to anyone who has owned a smartphone in the last decade. I can't remember the last time I took the time to type out the entire query on Google Search. I simply ask the question – and Google lays out the entire weather pattern for me. It saves me a ton of time and I can quickly glance at my screen and get back to work. But how does Google understand what I'm saying?

This article was originally published on October 26, 2016 and updated with new projects on 30th May, 2018. Data science projects offer you a promising way to kick-start your career in this field. Not only do you get to learn data science by applying it, you also get projects to showcase on your CV! Nowadays, recruiters evaluate a candidate's potential by his/her work and don't put a lot of emphasis on certifications. It wouldn't matter if you just tell them how much you know if you have nothing to show them! That's where most people struggle and miss out. You might have worked on several problems before, but if you can't make it presentable & easy-to-explain, how on earth would someone know what you are capable of?

Data science projects offer you a promising way to kick-start your analytics career. Not only you get to learn data science by applying, you also get projects to showcase on your CV. Nowadays, recruiters evaluate a candidate's potential by his/her work, not as much by certificates and resumes. It wouldn't matter, if you just tell them how much you know, if you have nothing to show them! That's where most people struggle and miss out!