I've written a classification algorithm that does a pretty good job at classifying some datasets. However, I compared my algorithm to other classification methods, and their results exceed my results by a little. For instance, when classifying a dataset from a repository, my algorithm is getting 95% correct while another algorithm usually gets 99% correct.
Should I continue to publish my results although 1) my algorithm is a little slower, and 2) my algorithm's results are not as good as the other results.
I'm a little torn. I'm excited as my paper and results are a contribution to the classification field as the algorithm is novel. Also, I'm of the stance that you can't beat EVERY algorithm. If we only published algorithms that could (loosely) beat other algorithms either A.) we'd never have new innovations, or B.) eventually every dataset would be 100% classified each time, or C.) every algorithm could instantaneously classify a dataset (speed).
I hope that my algorithm will continue to grow and others will pick it up and extend it. I hope that one day -- with tweaks -- my algorithm can reach 99% too.
I'm afraid of being rejected by the journal again. Yes, my first submission was rejected. One of the reasons for the rejection was that my dataset was small. However, when the dataset was small I was beating the other algorithms. Now, as the dataset has grown, the other algorithms are now beating me. I'd like not to be rejected again.