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I have a dataset with 120 features and 5000 instances. The dataset is combination of categorical and numerical values. It is a tabular dataset. My problem is a binary classification problem. I trained my dataset with all classic classification algorithms like Naive Bayes, Bayesian net, SVM, MLP, Random forest, Logistic regression etc. I would like to know is there any algorithms available in the machine learning field which is new and not the classic one and can be implemented using a tabular dataset.

I heard about Convolution neural network, deep neural network etc but I believe they are used in image data not tabular data.

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You can try Generalized Additive Models (GAM). It models the response variable, $y$, as a sum of functions of individual features $f_i(x_i)$: $y = \sum\limits_if_i(x_i)$. You don't need to provide $f_i$, the algorithm finds learns them from the data. By analyzing the $f_i$, you can see, which feature contributes significantly, and in which way. You can even fit the $f_i$ to simple analytic functions and get an analytic dependence of $y$ on $x_i$.

Links for GAM: GAM in wikipedia, GAM in R, GAM in Python, ClassificationGAM in Matlab, as well as here, here, here, and here.

A related method is Alternating Conditional Expectations (ACE) from this paper: link. I wrote a blog post about it here.

These methods are not new, but I have a feeling that you didn't try them.

I also recommend browsing through methods in weka. It has some interesting, non-mainstream algorithms, such as classifier for learning functional trees, HotSpot, alternating model trees, alternating decision trees, and many other.

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